diff --git a/.gitignore b/.gitignore index ed8c88c899..c2ac5137cb 100644 --- a/.gitignore +++ b/.gitignore @@ -95,4 +95,5 @@ test.py litellm_config.yaml .cursor .vscode/launch.json -litellm/proxy/to_delete_loadtest_work/* \ No newline at end of file +litellm/proxy/to_delete_loadtest_work/* +update_model_cost_map.py diff --git a/docs/my-website/docs/observability/helicone_integration.md b/docs/my-website/docs/observability/helicone_integration.md index 9b807b8d0f..27e972ddeb 100644 --- a/docs/my-website/docs/observability/helicone_integration.md +++ b/docs/my-website/docs/observability/helicone_integration.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Helicone - OSS LLM Observability Platform :::tip @@ -9,9 +12,68 @@ https://github.com/BerriAI/litellm [Helicone](https://helicone.ai/) is an open source observability platform that proxies your LLM requests and provides key insights into your usage, spend, latency and more. -## Using Helicone with LiteLLM +## Quick Start -LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily log data to Helicone based on the status of your responses. + + + +Use just 1 line of code to instantly log your responses **across all providers** with Helicone: + +```python +import os +from litellm import completion + +## Set env variables +os.environ["HELICONE_API_KEY"] = "your-helicone-key" +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# Set callbacks +litellm.success_callback = ["helicone"] + +# OpenAI call +response = completion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}], +) + +print(response) +``` + + + + +Add Helicone to your LiteLLM proxy configuration: + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + +# Add Helicone callback +litellm_settings: + success_callback: ["helicone"] + +# Set Helicone API key +environment_variables: + HELICONE_API_KEY: "your-helicone-key" +``` + +Start the proxy: +```bash +litellm --config config.yaml +``` + + + + +## Integration Methods + +There are two main approaches to integrate Helicone with LiteLLM: + +1. **Callbacks**: Log to Helicone while using any provider +2. **Proxy Mode**: Use Helicone as a proxy for advanced features ### Supported LLM Providers @@ -26,27 +88,16 @@ Helicone can log requests across [various LLM providers](https://docs.helicone.a - Replicate - And more -### Integration Methods +## Method 1: Using Callbacks -There are two main approaches to integrate Helicone with LiteLLM: +Log requests to Helicone while using any LLM provider directly. -1. Using callbacks -2. Using Helicone as a proxy - -Let's explore each method in detail. - -### Approach 1: Use Callbacks - -Use just 1 line of code to instantly log your responses **across all providers** with Helicone: - -```python -litellm.success_callback = ["helicone"] -``` - -Complete Code + + ```python import os +import litellm from litellm import completion ## Set env variables @@ -66,28 +117,78 @@ response = completion( print(response) ``` -### Approach 2: Use Helicone as a proxy + + + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + - model_name: claude-3 + litellm_params: + model: anthropic/claude-3-sonnet-20240229 + api_key: os.environ/ANTHROPIC_API_KEY + +# Add Helicone logging +litellm_settings: + success_callback: ["helicone"] + +# Environment variables +environment_variables: + HELICONE_API_KEY: "your-helicone-key" + OPENAI_API_KEY: "your-openai-key" + ANTHROPIC_API_KEY: "your-anthropic-key" +``` + +Start the proxy: +```bash +litellm --config config.yaml +``` + +Make requests to your proxy: +```python +import openai + +client = openai.OpenAI( + api_key="anything", # proxy doesn't require real API key + base_url="http://localhost:4000" +) + +response = client.chat.completions.create( + model="gpt-4", # This gets logged to Helicone + messages=[{"role": "user", "content": "Hello!"}] +) +``` + + + + +## Method 2: Using Helicone as a Proxy Helicone's proxy provides [advanced functionality](https://docs.helicone.ai/getting-started/proxy-vs-async) like caching, rate limiting, LLM security through [PromptArmor](https://promptarmor.com/) and more. -To use Helicone as a proxy for your LLM requests: + + -1. Set Helicone as your base URL via: litellm.api_base -2. Pass in Helicone request headers via: litellm.metadata - -Complete Code: +Set Helicone as your base URL and pass authentication headers: ```python import os import litellm from litellm import completion +# Configure LiteLLM to use Helicone proxy litellm.api_base = "https://oai.hconeai.com/v1" litellm.headers = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", } -response = litellm.completion( +# Set your OpenAI API key +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +response = completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "How does a court case get to the Supreme Court?"}] ) @@ -140,32 +241,112 @@ litellm.metadata = { Track multi-step and agentic LLM interactions using session IDs and paths: -```python -litellm.metadata = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API - "Helicone-Session-Id": "session-abc-123", # The session ID you want to track - "Helicone-Session-Path": "parent-trace/child-trace", # The path of the session -} -``` - -- `Helicone-Session-Id`: Use this to specify the unique identifier for the session you want to track. This allows you to group related requests together. -- `Helicone-Session-Path`: This header defines the path of the session, allowing you to represent parent and child traces. For example, "parent/child" represents a child trace of a parent trace. - -By using these two headers, you can effectively group and visualize multi-step LLM interactions, gaining insights into complex AI workflows. - -### Retry and Fallback Mechanisms - -Set up retry mechanisms and fallback options: + + ```python +import litellm + +litellm.api_base = "https://oai.hconeai.com/v1" litellm.metadata = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API - "Helicone-Retry-Enabled": "true", # Enable retry mechanism - "helicone-retry-num": "3", # Set number of retries - "helicone-retry-factor": "2", # Set exponential backoff factor - "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', # Set fallback models + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "parent-trace/child-trace", } + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Start a conversation"}] +) ``` + + + +```python +import openai + +client = openai.OpenAI( + api_key="anything", + base_url="http://localhost:4000" +) + +# First request in session +response1 = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}], + extra_headers={ + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "conversation/greeting" + } +) + +# Follow-up request in same session +response2 = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Tell me more"}], + extra_headers={ + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "conversation/follow-up" + } +) +``` + + + + +- `Helicone-Session-Id`: Unique identifier for the session to group related requests +- `Helicone-Session-Path`: Hierarchical path to represent parent/child traces (e.g., "parent/child") + +## Retry and Fallback Mechanisms + + + + +```python +import litellm + +litellm.api_base = "https://oai.hconeai.com/v1" +litellm.metadata = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", + "Helicone-Retry-Enabled": "true", + "helicone-retry-num": "3", + "helicone-retry-factor": "2", # Exponential backoff + "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', +} + +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}] +) +``` + + + + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + api_base: "https://oai.hconeai.com/v1" + +default_litellm_params: + headers: + Helicone-Auth: "Bearer ${HELICONE_API_KEY}" + Helicone-Retry-Enabled: "true" + helicone-retry-num: "3" + helicone-retry-factor: "2" + Helicone-Fallbacks: '["gpt-3.5-turbo", "gpt-4"]' + +environment_variables: + HELICONE_API_KEY: "your-helicone-key" + OPENAI_API_KEY: "your-openai-key" +``` + + + + > **Supported Headers** - For a full list of supported Helicone headers and their descriptions, please refer to the [Helicone documentation](https://docs.helicone.ai/getting-started/quick-start). > By utilizing these headers and metadata options, you can gain deeper insights into your LLM usage, optimize performance, and better manage your AI workflows with Helicone and LiteLLM. diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index c191b74226..165ef1d12f 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -889,6 +889,19 @@ curl http://0.0.0.0:4000/v1/chat/completions \ Example of using [Bedrock Guardrails with LiteLLM](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-use-converse-api.html) +### Selective Content Moderation with `guarded_text` + +LiteLLM supports selective content moderation using the `guarded_text` content type. This allows you to wrap only specific content that should be moderated by Bedrock Guardrails, rather than evaluating the entire conversation. + +**How it works:** +- Content with `type: "guarded_text"` gets automatically wrapped in `guardrailConverseContent` blocks +- Only the wrapped content is evaluated by Bedrock Guardrails +- Regular content with `type: "text"` bypasses guardrail evaluation + +:::note +If `guarded_text` is not used, the entire conversation history will be sent to the guardrail for evaluation, which can increase latency and costs. +::: + @@ -915,6 +928,24 @@ response = completion( "trace": "disabled", # The trace behavior for the guardrail. Can either be "disabled" or "enabled" }, ) + +# Selective guardrail usage with guarded_text - only specific content is evaluated +response_guard = completion( + model="anthropic.claude-v2", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is the main topic of this legal document?"}, + {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."} + ] + } + ], + guardrailConfig={ + "guardrailIdentifier": "gr-abc123", + "guardrailVersion": "DRAFT" + } +) ``` @@ -993,7 +1024,20 @@ response = client.chat.completions.create(model="bedrock-claude-v1", messages = temperature=0.7 ) -print(response) +# For adding selective guardrail usage with guarded_text +response_guard = client.chat.completions.create(model="bedrock-claude-v1", messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is the main topic of this legal document?"}, + {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."} + ] + } +], +temperature=0.7 +) + +print(response_guard) ``` diff --git a/docs/my-website/docs/providers/compactifai.md b/docs/my-website/docs/providers/compactifai.md index 0e6e8f4ed3..1aa8146307 100644 --- a/docs/my-website/docs/providers/compactifai.md +++ b/docs/my-website/docs/providers/compactifai.md @@ -4,7 +4,7 @@ import TabItem from '@theme/TabItem'; # CompactifAI https://docs.compactif.ai/ -CompactifAI offers highly compressed versions of leading language models, delivering up to **70% lower inference costs**, **4x throughput gains**, and **low-latency inference** with minimal quality loss (<5%). CompactifAI's OpenAI-compatible API makes integration straightforward, enabling developers to build ultra-efficient, scalable AI applications with superior concurrency and resource efficiency. +CompactifAI offers highly compressed versions of leading language models, delivering up to **70% lower inference costs**, **4x throughput gains**, and **low-latency inference** with minimal quality loss (under 5%). CompactifAI's OpenAI-compatible API makes integration straightforward, enabling developers to build ultra-efficient, scalable AI applications with superior concurrency and resource efficiency. | Property | Details | |-------|-------| @@ -192,7 +192,7 @@ Common model formats: ## Benefits - **Cost Efficient**: Up to 70% lower inference costs compared to standard models -- **High Performance**: 4x throughput gains with minimal quality loss (<5%) +- **High Performance**: 4x throughput gains with minimal quality loss (under 5%) - **Low Latency**: Optimized for fast response times - **Drop-in Replacement**: Full OpenAI API compatibility - **Scalable**: Superior concurrency and resource efficiency diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index fda0cee862..5a76189de9 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -2758,6 +2758,44 @@ curl http://localhost:4000/v1/fine_tuning/jobs \ +## Labels + + +Google enables you to add custom metadata to its `generateContent` and `streamGenerateContent` calls. +This mechanism is useful in Vertex AI because it allows costs and usage tracking over multiple +different applications or users. + + +### Usage + +You can use that feature through LiteLLM by sending `labels` or `metadata` field in your requests. + +If the client sets the `labels` field in the request to the LiteLLM, +the LiteLLM will pass the `labels` field to the Vertex AI backend. + +If the client sets the `metadata` field in the request to the LiteLLM and the `labels` field is not set, +the LiteLLM will create the `labels` field filled with `metadata` key/value pairs for all string values and +pass it to the Vertex AI backend. + + +Here is an example JSON request demonstrating the labels usage: + +```json +{ + "model": "gemini-2.0-flash-lite", + "messages": [ + { "role": "user", "content": "respond in 20 words. who are you?" } + ], + "labels": { + "client_app": "acme_comp_financial_app", + "department": "finance", + "project": "acme_ai" + } +} +``` + + + ## Extra ### Using `GOOGLE_APPLICATION_CREDENTIALS` diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index ee857f700a..8847566eb9 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -93,6 +93,8 @@ callback_settings: general_settings: completion_model: string + store_prompts_in_spend_logs: boolean + forward_client_headers_to_llm_api: boolean disable_spend_logs: boolean # turn off writing each transaction to the db disable_master_key_return: boolean # turn off returning master key on UI (checked on '/user/info' endpoint) disable_retry_on_max_parallel_request_limit_error: boolean # turn off retries when max parallel request limit is reached @@ -121,6 +123,35 @@ general_settings: alerting: ["slack", "email"] alerting_threshold: 0 use_client_credentials_pass_through_routes: boolean # use client credentials for all pass through routes like "/vertex-ai", /bedrock/. When this is True Virtual Key auth will not be applied on these endpoints + +router_settings: + routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle" - RECOMMENDED for best performance + redis_host: # string + redis_password: # string + redis_port: # string + enable_pre_call_checks: true # bool - Before call is made check if a call is within model context window + allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. + cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails + disable_cooldowns: True # bool - Disable cooldowns for all models + enable_tag_filtering: True # bool - Use tag based routing for requests + retry_policy: { # Dict[str, int]: retry policy for different types of exceptions + "AuthenticationErrorRetries": 3, + "TimeoutErrorRetries": 3, + "RateLimitErrorRetries": 3, + "ContentPolicyViolationErrorRetries": 4, + "InternalServerErrorRetries": 4 + } + allowed_fails_policy: { + "BadRequestErrorAllowedFails": 1000, # Allow 1000 BadRequestErrors before cooling down a deployment + "AuthenticationErrorAllowedFails": 10, # int + "TimeoutErrorAllowedFails": 12, # int + "RateLimitErrorAllowedFails": 10000, # int + "ContentPolicyViolationErrorAllowedFails": 15, # int + "InternalServerErrorAllowedFails": 20, # int + } + content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for content policy violations + fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for all errors + ``` ### litellm_settings - Reference diff --git a/docs/my-website/docs/proxy/logging_spec.md b/docs/my-website/docs/proxy/logging_spec.md index a39a62318e..5166b86ae1 100644 --- a/docs/my-website/docs/proxy/logging_spec.md +++ b/docs/my-website/docs/proxy/logging_spec.md @@ -61,6 +61,11 @@ Inherits from `StandardLoggingUserAPIKeyMetadata` and adds: | `requester_metadata` | `Optional[dict]` | Additional requester metadata | | `vector_store_request_metadata` | `Optional[List[StandardLoggingVectorStoreRequest]]` | Vector store request metadata | | `requester_custom_headers` | Dict[str, str] | Any custom (`x-`) headers sent by the client to the proxy. | +| `prompt_management_metadata` | `Optional[StandardLoggingPromptManagementMetadata]` | Prompt management and versioning metadata | +| `mcp_tool_call_metadata` | `Optional[StandardLoggingMCPToolCall]` | MCP (Model Context Protocol) tool call information and cost tracking | +| `applied_guardrails` | `Optional[List[str]]` | List of applied guardrail names | +| `usage_object` | `Optional[dict]` | Raw usage object from the LLM provider | +| `cold_storage_object_key` | `Optional[str]` | S3/GCS object key for cold storage retrieval | | `guardrail_information` | `Optional[StandardLoggingGuardrailInformation]` | Guardrail information | @@ -145,4 +150,82 @@ A literal type with two possible values: | `duration` | `Optional[float]` | Duration of the guardrail in seconds | | `masked_entity_count` | `Optional[Dict[str, int]]` | Count of masked entities | +## StandardLoggingPromptManagementMetadata +Used for tracking prompt versioning and management information. + +| Field | Type | Description | +|-------|------|-------------| +| `prompt_id` | `str` | **Required**. Unique identifier for the prompt template or version | +| `prompt_variables` | `Optional[dict]` | Variables/parameters used in the prompt template (e.g., `{"user_name": "John", "context": "support"}`) | +| `prompt_integration` | `str` | **Required**. Integration or system managing the prompt (e.g., `"langfuse"`, `"promptlayer"`, `"custom"`) | + +## StandardLoggingMCPToolCall + +Used to track Model Context Protocol (MCP) tool calls within LiteLLM requests. This provides detailed logging for external tool integrations. + +| Field | Type | Description | +|-------|------|-------------| +| `name` | `str` | **Required**. The name of the tool being called (e.g., `"get_weather"`, `"search_database"`) | +| `arguments` | `dict` | **Required**. Arguments passed to the tool as key-value pairs | +| `result` | `Optional[dict]` | The response/result returned by the tool execution (populated by custom logging hooks) | +| `mcp_server_name` | `Optional[str]` | Name of the MCP server that handled the tool call (e.g., `"weather-service"`, `"database-connector"`) | +| `mcp_server_logo_url` | `Optional[str]` | URL for the MCP server's logo (used for UI display in LiteLLM dashboard) | +| `namespaced_tool_name` | `Optional[str]` | Fully qualified tool name including server prefix (e.g., `"deepwiki-mcp/get_page_content"`, `"github-mcp/create_issue"`) | +| `mcp_server_cost_info` | `Optional[MCPServerCostInfo]` | Cost tracking information for the tool call | + +### MCPServerCostInfo + +Cost tracking structure for MCP server tool calls: + +| Field | Type | Description | +|-------|------|-------------| +| `default_cost_per_query` | `Optional[float]` | Default cost in USD for any tool call to this MCP server | +| `tool_name_to_cost_per_query` | `Optional[Dict[str, float]]` | Per-tool cost mapping for granular pricing (e.g., `{"search": 0.01, "create": 0.05}`) | + +### Usage + +```python +# Basic MCP tool call metadata +mcp_tool_call = { + "name": "search_documents", + "arguments": { + "query": "machine learning tutorials", + "limit": 10, + "filter": "type:pdf" + }, + "mcp_server_name": "document-search-service", + "namespaced_tool_name": "docs-mcp/search_documents", + "mcp_server_cost_info": { + "default_cost_per_query": 0.02, + "tool_name_to_cost_per_query": { + "search_documents": 0.02, + "get_document": 0.01 + } + } +} + +# optional result field (via custom logging hooks) +mcp_tool_call_with_result = { + "name": "search_documents", + "arguments": { + "query": "machine learning tutorials", + "limit": 10, + "filter": "type:pdf" + }, + "result": { + "documents": [...], + "total_found": 42, + "search_time_ms": 150 + }, + "mcp_server_name": "document-search-service", + "namespaced_tool_name": "docs-mcp/search_documents", + "mcp_server_cost_info": { + "default_cost_per_query": 0.02, + "tool_name_to_cost_per_query": { + "search_documents": 0.02, + "get_document": 0.01 + } + } +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md index b6a9c6a6b9..9d2b3757ee 100644 --- a/docs/my-website/docs/troubleshoot.md +++ b/docs/my-website/docs/troubleshoot.md @@ -2,7 +2,7 @@ [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -[Community Slack 💭](https://join.slack.com/share/enQtOTE0ODczMzk2Nzk4NC01YjUxNjY2YjBlYTFmNDRiZTM3NDFiYTM3MzVkODFiMDVjOGRjMmNmZTZkZTMzOWQzZGQyZWIwYjQ0MWExYmE3) +[Community Slack 💭](https://litellmossslack.slack.com/) Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ diff --git a/litellm/batches/main.py b/litellm/batches/main.py index e5c7a2e4b6..37b9aff4ef 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -340,7 +340,7 @@ def create_batch( @client async def aretrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -378,11 +378,129 @@ async def aretrieve_batch( except Exception as e: raise e +def _handle_retrieve_batch_providers_without_provider_config( + batch_id: str, + optional_params: GenericLiteLLMParams, + timeout: Union[float, httpx.Timeout], + litellm_params: dict, + _retrieve_batch_request: RetrieveBatchRequest, + _is_async: bool, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", +): + api_base: Optional[str] = None + if custom_llm_provider == "openai": + # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there + api_base = ( + optional_params.api_base + or litellm.api_base + or os.getenv("OPENAI_BASE_URL") + or os.getenv("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + optional_params.organization + or litellm.organization + or os.getenv("OPENAI_ORGANIZATION", None) + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + optional_params.api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or os.getenv("OPENAI_API_KEY") + ) + + response = openai_batches_instance.retrieve_batch( + _is_async=_is_async, + retrieve_batch_data=_retrieve_batch_request, + api_base=api_base, + api_key=api_key, + organization=organization, + timeout=timeout, + max_retries=optional_params.max_retries, + ) + elif custom_llm_provider == "azure": + api_base = ( + optional_params.api_base + or litellm.api_base + or get_secret_str("AZURE_API_BASE") + ) + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret_str("AZURE_API_VERSION") + ) + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret_str("AZURE_OPENAI_API_KEY") + or get_secret_str("AZURE_API_KEY") + ) + + extra_body = optional_params.get("extra_body", {}) + if extra_body is not None: + extra_body.pop("azure_ad_token", None) + else: + get_secret_str("AZURE_AD_TOKEN") # type: ignore + + response = azure_batches_instance.retrieve_batch( + _is_async=_is_async, + api_base=api_base, + api_key=api_key, + api_version=api_version, + timeout=timeout, + max_retries=optional_params.max_retries, + retrieve_batch_data=_retrieve_batch_request, + litellm_params=litellm_params, + ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret_str("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret_str("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret_str( + "VERTEXAI_CREDENTIALS" + ) + + response = vertex_ai_batches_instance.retrieve_batch( + _is_async=_is_async, + batch_id=batch_id, + api_base=api_base, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + vertex_credentials=vertex_credentials, + timeout=timeout, + max_retries=optional_params.max_retries, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( + custom_llm_provider + ), + model="n/a", + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + content="Unsupported provider", + request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + return response @client def retrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -430,115 +548,59 @@ def retrieve_batch( ) _is_async = kwargs.pop("aretrieve_batch", False) is True - api_base: Optional[str] = None - if custom_llm_provider == "openai": - # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there - api_base = ( - optional_params.api_base - or litellm.api_base - or os.getenv("OPENAI_BASE_URL") - or os.getenv("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - organization = ( - optional_params.organization - or litellm.organization - or os.getenv("OPENAI_ORGANIZATION", None) - or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 - ) - # set API KEY - api_key = ( - optional_params.api_key - or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there - or litellm.openai_key - or os.getenv("OPENAI_API_KEY") - ) - - response = openai_batches_instance.retrieve_batch( - _is_async=_is_async, - retrieve_batch_data=_retrieve_batch_request, - api_base=api_base, - api_key=api_key, - organization=organization, - timeout=timeout, - max_retries=optional_params.max_retries, - ) - elif custom_llm_provider == "azure": - api_base = ( - optional_params.api_base - or litellm.api_base - or get_secret_str("AZURE_API_BASE") - ) - api_version = ( - optional_params.api_version - or litellm.api_version - or get_secret_str("AZURE_API_VERSION") - ) - - api_key = ( - optional_params.api_key - or litellm.api_key - or litellm.azure_key - or get_secret_str("AZURE_OPENAI_API_KEY") - or get_secret_str("AZURE_API_KEY") - ) - - extra_body = optional_params.get("extra_body", {}) - if extra_body is not None: - extra_body.pop("azure_ad_token", None) - else: - get_secret_str("AZURE_AD_TOKEN") # type: ignore - - response = azure_batches_instance.retrieve_batch( - _is_async=_is_async, - api_base=api_base, - api_key=api_key, - api_version=api_version, - timeout=timeout, - max_retries=optional_params.max_retries, - retrieve_batch_data=_retrieve_batch_request, - litellm_params=litellm_params, - ) - elif custom_llm_provider == "vertex_ai": - api_base = optional_params.api_base or "" - vertex_ai_project = ( - optional_params.vertex_project - or litellm.vertex_project - or get_secret_str("VERTEXAI_PROJECT") - ) - vertex_ai_location = ( - optional_params.vertex_location - or litellm.vertex_location - or get_secret_str("VERTEXAI_LOCATION") - ) - vertex_credentials = optional_params.vertex_credentials or get_secret_str( - "VERTEXAI_CREDENTIALS" - ) - - response = vertex_ai_batches_instance.retrieve_batch( - _is_async=_is_async, - batch_id=batch_id, - api_base=api_base, - vertex_project=vertex_ai_project, - vertex_location=vertex_ai_location, - vertex_credentials=vertex_credentials, - timeout=timeout, - max_retries=optional_params.max_retries, + client = kwargs.get("client", None) + + # Try to use provider config first (for providers like bedrock) + model: Optional[str] = kwargs.get("model", None) + if model is not None: + provider_config = ProviderConfigManager.get_provider_batches_config( + model=model, + provider=LlmProviders(custom_llm_provider), ) else: - raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( - custom_llm_provider - ), - model="n/a", - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=400, - content="Unsupported provider", - request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + provider_config = None + + if provider_config is not None: + response = base_llm_http_handler.retrieve_batch( + batch_id=batch_id, + provider_config=provider_config, + litellm_params=litellm_params, + headers=extra_headers or {}, + api_base=optional_params.api_base, + api_key=optional_params.api_key, + logging_obj=litellm_logging_obj or LiteLLMLoggingObj( + model=model or "bedrock/unknown", + messages=[], + stream=False, + call_type="batch_retrieve", + start_time=None, + litellm_call_id="batch_retrieve_" + batch_id, + function_id="batch_retrieve", ), + _is_async=_is_async, + client=client + if client is not None + and isinstance(client, (HTTPHandler, AsyncHTTPHandler)) + else None, + timeout=timeout, + model=model, ) - return response + return response + + + ######################################################### + # Handle providers without provider config + ######################################################### + return _handle_retrieve_batch_providers_without_provider_config( + batch_id=batch_id, + custom_llm_provider=custom_llm_provider, + optional_params=optional_params, + litellm_params=litellm_params, + _retrieve_batch_request=_retrieve_batch_request, + _is_async=_is_async, + timeout=timeout, + ) + except Exception as e: raise e diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index adab0ca659..7ab82eb784 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -148,10 +148,22 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): ), ), } - verbose_logger.debug("payload %s", json.dumps(payload, indent=4)) + + # serialize datetime objects - for budget reset time in spend metrics + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + + try: + verbose_logger.debug("payload %s", safe_dumps(payload)) + except Exception as debug_error: + verbose_logger.debug( + "payload serialization failed: %s", str(debug_error) + ) + + json_payload = safe_dumps(payload) + response = await self.async_client.post( url=self.intake_url, - json=payload, + content=json_payload, headers={ "DD-API-KEY": self.DD_API_KEY, "Content-Type": "application/json", @@ -494,6 +506,12 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): latency_metrics = self._get_latency_metrics(standard_logging_payload) _metadata.update({"latency_metrics": dict(latency_metrics)}) + ######################################################### + # Add spend metrics to metadata + ######################################################### + spend_metrics = self._get_spend_metrics(standard_logging_payload) + _metadata.update({"spend_metrics": dict(spend_metrics)}) + ## extract tool calls and add to metadata tool_call_metadata = self._extract_tool_call_metadata(standard_logging_payload) _metadata.update(tool_call_metadata) @@ -543,6 +561,71 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): return latency_metrics + def _get_spend_metrics( + self, standard_logging_payload: StandardLoggingPayload + ) -> DDLLMObsSpendMetrics: + """ + Get the spend metrics from the standard logging payload + """ + spend_metrics: DDLLMObsSpendMetrics = DDLLMObsSpendMetrics() + + # send response cost + spend_metrics["response_cost"] = standard_logging_payload.get( + "response_cost", 0.0 + ) + + # Get budget information from metadata + metadata = standard_logging_payload.get("metadata", {}) + + # API key max budget + user_api_key_max_budget = metadata.get("user_api_key_max_budget") + if user_api_key_max_budget is not None: + spend_metrics["user_api_key_max_budget"] = float(user_api_key_max_budget) + + # API key spend + user_api_key_spend = metadata.get("user_api_key_spend") + if user_api_key_spend is not None: + try: + spend_metrics["user_api_key_spend"] = float(user_api_key_spend) + except (ValueError, TypeError): + verbose_logger.debug( + f"Invalid user_api_key_spend value: {user_api_key_spend}" + ) + + # API key budget reset datetime + user_api_key_budget_reset_at = metadata.get("user_api_key_budget_reset_at") + if user_api_key_budget_reset_at is not None: + try: + from datetime import datetime, timezone + + budget_reset_at = None + if isinstance(user_api_key_budget_reset_at, str): + # Handle ISO format strings that might have 'Z' suffix + iso_string = user_api_key_budget_reset_at.replace("Z", "+00:00") + budget_reset_at = datetime.fromisoformat(iso_string) + elif isinstance(user_api_key_budget_reset_at, datetime): + budget_reset_at = user_api_key_budget_reset_at + + if budget_reset_at is not None: + # Preserve timezone info if already present + if budget_reset_at.tzinfo is None: + budget_reset_at = budget_reset_at.replace(tzinfo=timezone.utc) + + # Convert to ISO string format for JSON serialization + # This prevents circular reference issues and ensures proper timezone representation + iso_string = budget_reset_at.isoformat() + spend_metrics["user_api_key_budget_reset_at"] = iso_string + + # Debug logging to verify the conversion + verbose_logger.debug( + f"Converted budget_reset_at to ISO format: {iso_string}" + ) + except Exception as e: + verbose_logger.debug(f"Error processing budget reset datetime: {e}") + verbose_logger.debug(f"Original value: {user_api_key_budget_reset_at}") + + return spend_metrics + def _process_input_messages_preserving_tool_calls( self, messages: List[Any] ) -> List[Dict[str, Any]]: diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 089fad62fb..69943a0fe4 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -15,7 +15,7 @@ from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.llms.custom_httpx.http_handler import _get_httpx_client from litellm.secret_managers.main import str_to_bool from litellm.types.integrations.langfuse import * -from litellm.types.llms.openai import HttpxBinaryResponseContent +from litellm.types.llms.openai import HttpxBinaryResponseContent, ResponsesAPIResponse from litellm.types.utils import ( EmbeddingResponse, ImageResponse, @@ -196,6 +196,7 @@ class LangFuseLogger: TranscriptionResponse, RerankResponse, HttpxBinaryResponseContent, + ResponsesAPIResponse, ], start_time: Optional[datetime] = None, end_time: Optional[datetime] = None, @@ -305,6 +306,7 @@ class LangFuseLogger: TranscriptionResponse, RerankResponse, HttpxBinaryResponseContent, + ResponsesAPIResponse, ], prompt: dict, level: str, @@ -369,6 +371,11 @@ class LangFuseLogger: ): input = prompt output = response_obj.results + elif response_obj is not None and isinstance( + response_obj, litellm.ResponsesAPIResponse + ): + input = prompt + output = self._get_responses_api_content_for_langfuse(response_obj) elif ( kwargs.get("call_type") is not None and kwargs.get("call_type") == "_arealtime" @@ -775,6 +782,19 @@ class LangFuseLogger: else: return None + @staticmethod + def _get_responses_api_content_for_langfuse( + response_obj: ResponsesAPIResponse, + ): + """ + Get the responses API content for Langfuse logging + """ + if hasattr(response_obj, 'output') and response_obj.output: + # ResponsesAPIResponse.output is a list of strings + return response_obj.output + else: + return None + @staticmethod def _get_langfuse_tags( standard_logging_object: Optional[StandardLoggingPayload], diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 4aeb9d4d64..7423e55b62 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -228,9 +228,11 @@ def safe_deep_copy(data): """ Safe Deep Copy - The LiteLLM Request has some object that can-not be pickled / deep copied - - Use this function to safely deep copy the LiteLLM Request + The LiteLLM request may contain objects that cannot be pickled/deep-copied + (e.g., tracing spans, locks, clients). + + This helper deep-copies each top-level key independently; on failure keeps + original ref """ import copy @@ -255,9 +257,22 @@ def safe_deep_copy(data): "litellm_parent_otel_span" ) data["litellm_metadata"]["litellm_parent_otel_span"] = "placeholder" - new_data = copy.deepcopy(data) - # Step 2: re-add the litellm_parent_otel_span after doing a deep copy + # Step 2: Per-key deepcopy with fallback + if isinstance(data, dict): + new_data = {} + for k, v in data.items(): + try: + new_data[k] = copy.deepcopy(v) + except Exception: + new_data[k] = v + else: + try: + new_data = copy.deepcopy(data) + except Exception: + new_data = data + + # Step 3: re-add the litellm_parent_otel_span after doing a deep copy if isinstance(data, dict) and litellm_parent_otel_span is not None: if "metadata" in data and "litellm_parent_otel_span" in data["metadata"]: data["metadata"]["litellm_parent_otel_span"] = litellm_parent_otel_span @@ -268,4 +283,4 @@ def safe_deep_copy(data): data["litellm_metadata"][ "litellm_parent_otel_span" ] = litellm_parent_otel_span - return new_data + return new_data \ No newline at end of file diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 2f21d28089..0bca172097 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -300,9 +300,9 @@ class Logging(LiteLLMLoggingBaseClass): self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4()) self.function_id = function_id self.streaming_chunks: List[Any] = [] # for generating complete stream response - self.sync_streaming_chunks: List[Any] = ( - [] - ) # for generating complete stream response + self.sync_streaming_chunks: List[ + Any + ] = [] # for generating complete stream response self.log_raw_request_response = log_raw_request_response # Initialize dynamic callbacks @@ -672,24 +672,23 @@ class Logging(LiteLLMLoggingBaseClass): if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook( non_default_params ): - self.model_call_details["prompt_integration"] = ( - anthropic_cache_control_logger.__class__.__name__ - ) + self.model_call_details[ + "prompt_integration" + ] = anthropic_cache_control_logger.__class__.__name__ return anthropic_cache_control_logger ######################################################### # Vector Store / Knowledge Base hooks ######################################################### if litellm.vector_store_registry is not None: - vector_store_custom_logger = _init_custom_logger_compatible_class( logging_integration="vector_store_pre_call_hook", internal_usage_cache=None, llm_router=None, ) - self.model_call_details["prompt_integration"] = ( - vector_store_custom_logger.__class__.__name__ - ) + self.model_call_details[ + "prompt_integration" + ] = vector_store_custom_logger.__class__.__name__ return vector_store_custom_logger return None @@ -741,9 +740,9 @@ class Logging(LiteLLMLoggingBaseClass): model ): # if model name was changes pre-call, overwrite the initial model call name with the new one self.model_call_details["model"] = model - self.model_call_details["litellm_params"]["api_base"] = ( - self._get_masked_api_base(additional_args.get("api_base", "")) - ) + self.model_call_details["litellm_params"][ + "api_base" + ] = self._get_masked_api_base(additional_args.get("api_base", "")) def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915 # Log the exact input to the LLM API @@ -772,10 +771,10 @@ class Logging(LiteLLMLoggingBaseClass): try: # [Non-blocking Extra Debug Information in metadata] if turn_off_message_logging is True: - _metadata["raw_request"] = ( - "redacted by litellm. \ + _metadata[ + "raw_request" + ] = "redacted by litellm. \ 'litellm.turn_off_message_logging=True'" - ) else: curl_command = self._get_request_curl_command( api_base=additional_args.get("api_base", ""), @@ -786,32 +785,32 @@ class Logging(LiteLLMLoggingBaseClass): _metadata["raw_request"] = str(curl_command) # split up, so it's easier to parse in the UI - self.model_call_details["raw_request_typed_dict"] = ( - RawRequestTypedDict( - raw_request_api_base=str( - additional_args.get("api_base") or "" - ), - raw_request_body=self._get_raw_request_body( - additional_args.get("complete_input_dict", {}) - ), - raw_request_headers=self._get_masked_headers( - additional_args.get("headers", {}) or {}, - ignore_sensitive_headers=True, - ), - error=None, - ) + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + raw_request_api_base=str( + additional_args.get("api_base") or "" + ), + raw_request_body=self._get_raw_request_body( + additional_args.get("complete_input_dict", {}) + ), + raw_request_headers=self._get_masked_headers( + additional_args.get("headers", {}) or {}, + ignore_sensitive_headers=True, + ), + error=None, ) except Exception as e: - self.model_call_details["raw_request_typed_dict"] = ( - RawRequestTypedDict( - error=str(e), - ) + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + error=str(e), ) - _metadata["raw_request"] = ( - "Unable to Log \ + _metadata[ + "raw_request" + ] = "Unable to Log \ raw request: {}".format( - str(e) - ) + str(e) ) if getattr(self, "logger_fn", None) and callable(self.logger_fn): try: @@ -1112,13 +1111,13 @@ class Logging(LiteLLMLoggingBaseClass): for callback in callbacks: try: if isinstance(callback, CustomLogger): - response: Optional[MCPPostCallResponseObject] = ( - await callback.async_post_mcp_tool_call_hook( - kwargs=kwargs, - response_obj=post_mcp_tool_call_response_obj, - start_time=start_time, - end_time=end_time, - ) + response: Optional[ + MCPPostCallResponseObject + ] = await callback.async_post_mcp_tool_call_hook( + kwargs=kwargs, + response_obj=post_mcp_tool_call_response_obj, + start_time=start_time, + end_time=end_time, ) ###################################################################### # if any of the callbacks modify the response, use the modified response @@ -1238,9 +1237,9 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) - self.model_call_details["response_cost_failure_debug_information"] = ( - debug_info - ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info return None try: @@ -1265,9 +1264,9 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) - self.model_call_details["response_cost_failure_debug_information"] = ( - debug_info - ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info return None @@ -1411,9 +1410,9 @@ class Logging(LiteLLMLoggingBaseClass): end_time = datetime.datetime.now() if self.completion_start_time is None: self.completion_start_time = end_time - self.model_call_details["completion_start_time"] = ( - self.completion_start_time - ) + self.model_call_details[ + "completion_start_time" + ] = self.completion_start_time self.model_call_details["log_event_type"] = "successful_api_call" self.model_call_details["end_time"] = end_time self.model_call_details["cache_hit"] = cache_hit @@ -1466,39 +1465,39 @@ class Logging(LiteLLMLoggingBaseClass): "response_cost" ] else: - self.model_call_details["response_cost"] = ( - self._response_cost_calculator(result=logging_result) - ) + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator(result=logging_result) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=logging_result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=logging_result, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) elif isinstance(result, dict) or isinstance(result, list): ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=result, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) elif standard_logging_object is not None: - self.model_call_details["standard_logging_object"] = ( - standard_logging_object - ) + self.model_call_details[ + "standard_logging_object" + ] = standard_logging_object else: # streaming chunks + image gen. self.model_call_details["response_cost"] = None @@ -1597,7 +1596,6 @@ class Logging(LiteLLMLoggingBaseClass): ) if complete_streaming_response is not None: - self.success_handler(result=complete_streaming_response) return @@ -1650,23 +1648,23 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( "Logging Details LiteLLM-Success Call streaming complete" ) - self.model_call_details["complete_streaming_response"] = ( - complete_streaming_response - ) - self.model_call_details["response_cost"] = ( - self._response_cost_calculator(result=complete_streaming_response) - ) + self.model_call_details[ + "complete_streaming_response" + ] = complete_streaming_response + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator(result=complete_streaming_response) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=complete_streaming_response, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_success_callbacks, @@ -1994,10 +1992,10 @@ class Logging(LiteLLMLoggingBaseClass): ) else: if self.stream and complete_streaming_response: - self.model_call_details["complete_response"] = ( - self.model_call_details.get( - "complete_streaming_response", {} - ) + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} ) result = self.model_call_details["complete_response"] openMeterLogger.log_success_event( @@ -2036,10 +2034,10 @@ class Logging(LiteLLMLoggingBaseClass): ) else: if self.stream and complete_streaming_response: - self.model_call_details["complete_response"] = ( - self.model_call_details.get( - "complete_streaming_response", {} - ) + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} ) result = self.model_call_details["complete_response"] @@ -2141,10 +2139,12 @@ class Logging(LiteLLMLoggingBaseClass): result.usage = batch_usage elif not is_base64_unified_file_id: # only run for non-unified file ids - response_cost, batch_usage, batch_models = ( - await _handle_completed_batch( - batch=result, custom_llm_provider=self.custom_llm_provider - ) + ( + response_cost, + batch_usage, + batch_models, + ) = await _handle_completed_batch( + batch=result, custom_llm_provider=self.custom_llm_provider ) result._hidden_params["response_cost"] = response_cost @@ -2175,9 +2175,9 @@ class Logging(LiteLLMLoggingBaseClass): if complete_streaming_response is not None: print_verbose("Async success callbacks: Got a complete streaming response") - self.model_call_details["async_complete_streaming_response"] = ( - complete_streaming_response - ) + self.model_call_details[ + "async_complete_streaming_response" + ] = complete_streaming_response try: if self.model_call_details.get("cache_hit", False) is True: @@ -2188,10 +2188,10 @@ class Logging(LiteLLMLoggingBaseClass): model_call_details=self.model_call_details ) # base_model defaults to None if not set on model_info - self.model_call_details["response_cost"] = ( - self._response_cost_calculator( - result=complete_streaming_response - ) + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator( + result=complete_streaming_response ) verbose_logger.debug( @@ -2204,16 +2204,16 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["response_cost"] = None ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=complete_streaming_response, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_async_success_callbacks, @@ -2426,18 +2426,18 @@ class Logging(LiteLLMLoggingBaseClass): ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj={}, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="failure", - error_str=str(exception), - original_exception=exception, - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj={}, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="failure", + error_str=str(exception), + original_exception=exception, + standard_built_in_tools_params=self.standard_built_in_tools_params, ) return start_time, end_time @@ -3326,9 +3326,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 endpoint=arize_config.endpoint, ) - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - f"space_id={arize_config.space_key},api_key={arize_config.api_key}" - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"space_id={arize_config.space_key},api_key={arize_config.api_key}" for callback in _in_memory_loggers: if ( isinstance(callback, ArizeLogger) @@ -3352,9 +3352,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 # auth can be disabled on local deployments of arize phoenix if arize_phoenix_config.otlp_auth_headers is not None: - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - arize_phoenix_config.otlp_auth_headers - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = arize_phoenix_config.otlp_auth_headers for callback in _in_memory_loggers: if ( @@ -3462,9 +3462,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 exporter="otlp_http", endpoint="https://langtrace.ai/api/trace", ) - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - f"api_key={os.getenv('LANGTRACE_API_KEY')}" - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}" for callback in _in_memory_loggers: if ( isinstance(callback, OpenTelemetry) @@ -4114,10 +4114,10 @@ class StandardLoggingPayloadSetup: for key in StandardLoggingHiddenParams.__annotations__.keys(): if key in hidden_params: if key == "additional_headers": - clean_hidden_params["additional_headers"] = ( - StandardLoggingPayloadSetup.get_additional_headers( - hidden_params[key] - ) + clean_hidden_params[ + "additional_headers" + ] = StandardLoggingPayloadSetup.get_additional_headers( + hidden_params[key] ) else: clean_hidden_params[key] = hidden_params[key] # type: ignore @@ -4150,15 +4150,28 @@ class StandardLoggingPayloadSetup: from litellm.integrations.s3 import get_s3_object_key # Only generate object key if cold storage is configured - if litellm.configured_cold_storage_logger is None: + configured_cold_storage_logger = litellm.configured_cold_storage_logger + if configured_cold_storage_logger is None: return None try: # Generate file name in same format as litellm.utils.get_logging_id s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}" + # Get the actual s3_path from the configured cold storage logger instance + s3_path = "" # default value + + # Try to get the actual logger instance from the logger name + try: + custom_logger = litellm.logging_callback_manager.get_active_custom_logger_for_callback_name(configured_cold_storage_logger) + if custom_logger and hasattr(custom_logger, 's3_path') and custom_logger.s3_path: + s3_path = custom_logger.s3_path + except Exception: + # If any error occurs in getting the logger instance, use default empty s3_path + pass + s3_object_key = get_s3_object_key( - s3_path="", # Use empty path as default + s3_path=s3_path, # Use actual s3_path from logger configuration team_alias_prefix="", # Don't split by team alias for cold storage start_time=start_time, s3_file_name=s3_file_name, @@ -4553,6 +4566,9 @@ def get_standard_logging_metadata( clean_metadata = StandardLoggingMetadata( user_api_key_hash=None, user_api_key_alias=None, + user_api_key_spend=None, + user_api_key_max_budget=None, + user_api_key_budget_reset_at=None, user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_user_id=None, @@ -4602,9 +4618,9 @@ def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]): ): for k, v in metadata["user_api_key_metadata"].items(): if k == "logging": # prevent logging user logging keys - cleaned_user_api_key_metadata[k] = ( - "scrubbed_by_litellm_for_sensitive_keys" - ) + cleaned_user_api_key_metadata[ + k + ] = "scrubbed_by_litellm_for_sensitive_keys" else: cleaned_user_api_key_metadata[k] = v diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 65f49cf08b..356d48dcb8 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -16,8 +16,8 @@ from litellm import verbose_logger from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client from litellm.types.files import get_file_extension_from_mime_type from litellm.types.llms.anthropic import * -from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock from litellm.types.llms.bedrock import CachePointBlock +from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock from litellm.types.llms.custom_http import httpxSpecialProvider from litellm.types.llms.ollama import OllamaVisionModelObject from litellm.types.llms.openai import ( @@ -1067,10 +1067,10 @@ def convert_to_gemini_tool_call_invoke( if tool_calls is not None: for tool in tool_calls: if "function" in tool: - gemini_function_call: Optional[VertexFunctionCall] = ( - _gemini_tool_call_invoke_helper( - function_call_params=tool["function"] - ) + gemini_function_call: Optional[ + VertexFunctionCall + ] = _gemini_tool_call_invoke_helper( + function_call_params=tool["function"] ) if gemini_function_call is not None: _parts_list.append( @@ -1589,9 +1589,9 @@ def anthropic_messages_pt( # noqa: PLR0915 ) if "cache_control" in _content_element: - _anthropic_content_element["cache_control"] = ( - _content_element["cache_control"] - ) + _anthropic_content_element[ + "cache_control" + ] = _content_element["cache_control"] user_content.append(_anthropic_content_element) elif m.get("type", "") == "text": m = cast(ChatCompletionTextObject, m) @@ -1629,9 +1629,9 @@ def anthropic_messages_pt( # noqa: PLR0915 ) if "cache_control" in _content_element: - _anthropic_content_text_element["cache_control"] = ( - _content_element["cache_control"] - ) + _anthropic_content_text_element[ + "cache_control" + ] = _content_element["cache_control"] user_content.append(_anthropic_content_text_element) @@ -2482,8 +2482,7 @@ class BedrockImageProcessor: if is_document: return BedrockImageProcessor._get_document_format( - mime_type=mime_type, - supported_doc_formats=supported_doc_formats + mime_type=mime_type, supported_doc_formats=supported_doc_formats ) else: @@ -2495,12 +2494,9 @@ class BedrockImageProcessor: f"Unsupported image format: {image_format}. Supported formats: {supported_image_and_video_formats}" ) return image_format - + @staticmethod - def _get_document_format( - mime_type: str, - supported_doc_formats: List[str] - ) -> str: + def _get_document_format(mime_type: str, supported_doc_formats: List[str]) -> str: """ Get the document format from the mime type @@ -2519,13 +2515,9 @@ class BedrockImageProcessor: The document format """ valid_extensions: Optional[List[str]] = None - potential_extensions = mimetypes.guess_all_extensions( - mime_type, strict=False - ) + potential_extensions = mimetypes.guess_all_extensions(mime_type, strict=False) valid_extensions = [ - ext[1:] - for ext in potential_extensions - if ext[1:] in supported_doc_formats + ext[1:] for ext in potential_extensions if ext[1:] in supported_doc_formats ] # Fallback to types/files.py if mimetypes doesn't return valid extensions @@ -2689,10 +2681,12 @@ def _convert_to_bedrock_tool_call_invoke( ) bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool) _parts_list.append(bedrock_content_block) - + # Check for cache_control and add a separate cachePoint block if tool.get("cache_control", None) is not None: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) _parts_list.append(cache_point_block) return _parts_list except Exception as e: @@ -2754,7 +2748,7 @@ def _convert_to_bedrock_tool_call_result( for content in content_list: if content["type"] == "text": content_str += content["text"] - + message.get("name", "") id = str(message.get("tool_call_id", str(uuid.uuid4()))) @@ -2763,7 +2757,7 @@ def _convert_to_bedrock_tool_call_result( content=[tool_result_content_block], toolUseId=id, ) - + content_block = BedrockContentBlock(toolResult=tool_result) return content_block @@ -3085,6 +3079,7 @@ class BedrockConverseMessagesProcessor: messages.append(DEFAULT_USER_CONTINUE_MESSAGE) return messages + @staticmethod async def _bedrock_converse_messages_pt_async( # noqa: PLR0915 messages: List, @@ -3128,6 +3123,12 @@ class BedrockConverseMessagesProcessor: if element["type"] == "text": _part = BedrockContentBlock(text=element["text"]) _parts.append(_part) + elif element["type"] == "guarded_text": + # Wrap guarded_text in guardrailConverseContent block + _part = BedrockContentBlock( + guardrailConverseContent={"text": element["text"]} + ) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -3170,6 +3171,7 @@ class BedrockConverseMessagesProcessor: msg_i += 1 if user_content: + if len(contents) > 0 and contents[-1]["role"] == "user": if ( assistant_continue_message is not None @@ -3199,26 +3201,29 @@ class BedrockConverseMessagesProcessor: current_message = messages[msg_i] tool_call_result = _convert_to_bedrock_tool_call_result(current_message) tool_content.append(tool_call_result) - + # Check if we need to add a separate cachePoint block has_cache_control = False - + # Check for message-level cache_control if current_message.get("cache_control", None) is not None: has_cache_control = True # Check for content-level cache_control in list content elif isinstance(current_message.get("content"), list): for content_element in current_message["content"]: - if (isinstance(content_element, dict) and - content_element.get("cache_control", None) is not None): + if ( + isinstance(content_element, dict) + and content_element.get("cache_control", None) is not None + ): has_cache_control = True break - + # Add a separate cachePoint block if cache_control is present if has_cache_control: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) tool_content.append(cache_point_block) - msg_i += 1 if tool_content: @@ -3299,7 +3304,7 @@ class BedrockConverseMessagesProcessor: image_url=image_url ) assistants_parts.append(assistants_part) - # Add cache point block for assistant content elements + # Add cache point block for assistant content elements _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( message_block=cast( @@ -3311,8 +3316,12 @@ class BedrockConverseMessagesProcessor: if _cache_point_block is not None: assistants_parts.append(_cache_point_block) assistant_content.extend(assistants_parts) - elif _assistant_content is not None and isinstance(_assistant_content, str): - assistant_content.append(BedrockContentBlock(text=_assistant_content)) + elif _assistant_content is not None and isinstance( + _assistant_content, str + ): + assistant_content.append( + BedrockContentBlock(text=_assistant_content) + ) # Add cache point block for assistant string content _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( @@ -3496,6 +3505,12 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 if element["type"] == "text": _part = BedrockContentBlock(text=element["text"]) _parts.append(_part) + elif element["type"] == "guarded_text": + # Wrap guarded_text in guardrailConverseContent block + _part = BedrockContentBlock( + guardrailConverseContent={"text": element["text"]} + ) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -3539,6 +3554,7 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 msg_i += 1 if user_content: + if len(contents) > 0 and contents[-1]["role"] == "user": if ( assistant_continue_message is not None @@ -3565,29 +3581,33 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 while msg_i < len(messages) and messages[msg_i]["role"] == "tool": tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) current_message = messages[msg_i] - + # Add the tool result first tool_content.append(tool_call_result) - + # Check if we need to add a separate cachePoint block has_cache_control = False - + # Check for message-level cache_control if current_message.get("cache_control", None) is not None: has_cache_control = True # Check for content-level cache_control in list content elif isinstance(current_message.get("content"), list): for content_element in current_message["content"]: - if (isinstance(content_element, dict) and - content_element.get("cache_control", None) is not None): + if ( + isinstance(content_element, dict) + and content_element.get("cache_control", None) is not None + ): has_cache_control = True break - + # Add a separate cachePoint block if cache_control is present if has_cache_control: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) tool_content.append(cache_point_block) - + msg_i += 1 if tool_content: # if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles) @@ -3852,10 +3872,9 @@ def function_call_prompt(messages: list, functions: list): if isinstance(message["content"], str): message["content"] += f""" {function_prompt}""" else: - message["content"].append({ - "type": "text", - "text": f""" {function_prompt}""" - }) + message["content"].append( + {"type": "text", "text": f""" {function_prompt}"""} + ) function_added_to_prompt = True if function_added_to_prompt is False: diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index ce874bfde9..b3b5f5107b 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -200,8 +200,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) _allowed_properties = set(AnthropicInputSchema.__annotations__.keys()) - input_schema_filtered = {k: v for k, v in _input_schema.items() if k in _allowed_properties} - input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema(**input_schema_filtered) + input_schema_filtered = { + k: v for k, v in _input_schema.items() if k in _allowed_properties + } + input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema( + **input_schema_filtered + ) _tool = AnthropicMessagesTool( name=tool["function"]["name"], diff --git a/litellm/llms/base_llm/batches/transformation.py b/litellm/llms/base_llm/batches/transformation.py index 1d3e54fae6..9e67689fcd 100644 --- a/litellm/llms/base_llm/batches/transformation.py +++ b/litellm/llms/base_llm/batches/transformation.py @@ -158,6 +158,48 @@ class BaseBatchesConfig(ABC): """ pass + @abstractmethod + def transform_retrieve_batch_request( + self, + batch_id: str, + optional_params: dict, + litellm_params: dict, + ) -> Union[bytes, str, Dict[str, Any]]: + """ + Transform the batch retrieval request to provider-specific format. + + Args: + batch_id: Batch ID to retrieve + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data + """ + pass + + @abstractmethod + def transform_retrieve_batch_response( + self, + model: Optional[str], + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform provider-specific batch retrieval response to LiteLLM format. + + Args: + model: Model name + raw_response: Raw HTTP response + logging_obj: Logging object + litellm_params: LiteLLM parameters + + Returns: + LiteLLM batch object + """ + pass + @abstractmethod def get_error_class( self, error_message: str, status_code: int, headers: Union[Dict, Headers] diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index 3338450f79..2f3d00dddd 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -7,7 +7,6 @@ from httpx import Headers, Response from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.types.llms.bedrock import ( - BedrockBatchJobStatus, BedrockCreateBatchRequest, BedrockCreateBatchResponse, BedrockInputDataConfig, @@ -200,19 +199,23 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # Extract information from typed Bedrock response job_arn = response_data.get("jobArn", "") - status: BedrockBatchJobStatus = response_data.get("status", "Submitted") + status_str: str = str(response_data.get("status", "Submitted")) # Map Bedrock status to OpenAI-compatible status - status_mapping: Dict[BedrockBatchJobStatus, str] = { + status_mapping: Dict[str, str] = { "Submitted": "validating", + "Validating": "validating", + "Scheduled": "in_progress", "InProgress": "in_progress", + "PartiallyCompleted": "completed", "Completed": "completed", "Failed": "failed", "Stopping": "cancelling", - "Stopped": "cancelled" + "Stopped": "cancelled", + "Expired": "expired", } - openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status, "validating")) + openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating")) # Get original request data from litellm_params if available original_request = litellm_params.get("original_batch_request", {}) @@ -229,7 +232,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): output_file_id=None, # Will be populated when job completes error_file_id=None, created_at=int(time.time()), - in_progress_at=int(time.time()) if status == "InProgress" else None, + in_progress_at=int(time.time()) if status_str == "InProgress" else None, expires_at=None, finalizing_at=None, completed_at=None, @@ -241,6 +244,203 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): metadata=original_request.get("metadata", {}), ) + def transform_retrieve_batch_request( + self, + batch_id: str, + optional_params: dict, + litellm_params: dict, + ) -> Dict[str, Any]: + """ + Transform batch retrieval request for Bedrock. + + Args: + batch_id: Bedrock job ARN + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data for Bedrock GetModelInvocationJob API + """ + # For Bedrock, batch_id should be the full job ARN + # The GetModelInvocationJob API expects the full ARN as the identifier + if not batch_id.startswith("arn:aws:bedrock:"): + raise ValueError(f"Invalid batch_id format. Expected ARN, got: {batch_id}") + + # Extract the job identifier from the ARN - use the full ARN path part + # ARN format: arn:aws:bedrock:region:account:model-invocation-job/job-name + arn_parts = batch_id.split(":") + if len(arn_parts) < 6: + raise ValueError(f"Invalid ARN format: {batch_id}") + + region = arn_parts[3] + # arn_parts[5] contains "model-invocation-job/{jobId}" + + # Build the endpoint URL for GetModelInvocationJob + # AWS API format: GET /model-invocation-job/{jobIdentifier} + # Use the FULL ARN as jobIdentifier and URL-encode it (includes ':' and '/') + import urllib.parse as _ul + encoded_arn = _ul.quote(batch_id, safe="") + endpoint_url = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}" + + # Use common utility for AWS signing + signed_headers, _ = self.common_utils.sign_aws_request( + service_name="bedrock", + data={}, # GET request has no body + endpoint_url=endpoint_url, + optional_params=optional_params, + method="GET" + ) + + # Return pre-signed request format + return { + "method": "GET", + "url": endpoint_url, + "headers": signed_headers, + "data": None + } + + def _parse_timestamps_and_status(self, response_data, status_str: str): + """Helper to parse timestamps based on status.""" + import datetime + def parse_timestamp(ts_str: Optional[str]) -> Optional[int]: + if not ts_str: + return None + try: + dt = datetime.datetime.fromisoformat(ts_str.replace('Z', '+00:00')) + return int(dt.timestamp()) + except Exception: + return None + + created_at = parse_timestamp(str(response_data.get("submitTime")) if response_data.get("submitTime") is not None else None) + in_progress_states = {"InProgress", "Validating", "Scheduled"} + in_progress_at = ( + parse_timestamp(str(response_data.get("lastModifiedTime")) if response_data.get("lastModifiedTime") is not None else None) + if status_str in in_progress_states + else None + ) + completed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str in {"Completed", "PartiallyCompleted"} else None + failed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Failed" else None + cancelled_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Stopped" else None + expires_at = parse_timestamp(str(response_data.get("jobExpirationTime")) if response_data.get("jobExpirationTime") is not None else None) + + return created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at + + def _extract_file_configs(self, response_data): + """Helper to extract input and output file configurations.""" + # Extract input file ID + input_file_id = "" + input_data_config = response_data.get("inputDataConfig", {}) + if isinstance(input_data_config, dict): + s3_input_config = input_data_config.get("s3InputDataConfig", {}) + if isinstance(s3_input_config, dict): + input_file_id = s3_input_config.get("s3Uri", "") + + # Extract output file ID + output_file_id = None + output_data_config = response_data.get("outputDataConfig", {}) + if isinstance(output_data_config, dict): + s3_output_config = output_data_config.get("s3OutputDataConfig", {}) + if isinstance(s3_output_config, dict): + output_file_id = s3_output_config.get("s3Uri", "") + + return input_file_id, output_file_id + + def _extract_errors_and_metadata(self, response_data, raw_response): + """Helper to extract errors and enriched metadata.""" + # Extract errors + message = response_data.get("message") + errors = None + if message: + from openai.types.batch import Errors + from openai.types.batch_error import BatchError + errors = Errors( + data=[BatchError(message=message, code=str(raw_response.status_code))], + object="list" + ) + + # Enrich metadata with useful Bedrock fields + enriched_metadata_raw: Dict[str, Any] = { + "jobName": response_data.get("jobName"), + "clientRequestToken": response_data.get("clientRequestToken"), + "modelId": response_data.get("modelId"), + "roleArn": response_data.get("roleArn"), + "timeoutDurationInHours": response_data.get("timeoutDurationInHours"), + "vpcConfig": response_data.get("vpcConfig"), + } + import json as _json + enriched_metadata: Dict[str, str] = {} + for _k, _v in enriched_metadata_raw.items(): + if _v is None: + continue + if isinstance(_v, (dict, list)): + try: + enriched_metadata[_k] = _json.dumps(_v) + except Exception: + enriched_metadata[_k] = str(_v) + else: + enriched_metadata[_k] = str(_v) + + return errors, enriched_metadata + + def transform_retrieve_batch_response( + self, + model: Optional[str], + raw_response: Response, + logging_obj: Any, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform Bedrock batch retrieval response to LiteLLM format. + """ + from litellm.types.llms.bedrock import BedrockGetBatchResponse + try: + response_data: BedrockGetBatchResponse = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse Bedrock batch response: {e}") + + job_arn = response_data.get("jobArn", "") + status_str: str = str(response_data.get("status", "Submitted")) + + # Map Bedrock status to OpenAI-compatible status + status_mapping: Dict[str, str] = { + "Submitted": "validating", "Validating": "validating", "Scheduled": "in_progress", + "InProgress": "in_progress", "PartiallyCompleted": "completed", "Completed": "completed", + "Failed": "failed", "Stopping": "cancelling", "Stopped": "cancelled", "Expired": "expired" + } + openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating")) + + # Parse timestamps + created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at = self._parse_timestamps_and_status(response_data, status_str) + + # Extract file configurations + input_file_id, output_file_id = self._extract_file_configs(response_data) + + # Extract errors and metadata + errors, enriched_metadata = self._extract_errors_and_metadata(response_data, raw_response) + + return LiteLLMBatch( + id=job_arn, + object="batch", + endpoint="/v1/chat/completions", + errors=errors, + input_file_id=input_file_id, + completion_window="24h", + status=openai_status, + output_file_id=output_file_id, + error_file_id=None, + created_at=created_at or int(time.time()), + in_progress_at=in_progress_at, + expires_at=expires_at, + finalizing_at=None, + completed_at=completed_at, + failed_at=failed_at, + expired_at=None, + cancelling_at=None, + cancelled_at=cancelled_at, + request_counts=None, + metadata=enriched_metadata, + ) + def get_error_class( self, error_message: str, status_code: int, headers: Union[Dict, Headers] ) -> BaseLLMException: diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index e3d65be8bb..88103a86cf 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -501,7 +501,6 @@ class AmazonConverseConfig(BaseConfig): ) and not is_thinking_enabled ): - optional_params["tool_choice"] = ToolChoiceValuesBlock( tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) ) @@ -995,7 +994,9 @@ class AmazonConverseConfig(BaseConfig): return message, returned_finish_reason - def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[ + def _translate_message_content( + self, content_blocks: List[ContentBlock] + ) -> Tuple[ str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], @@ -1010,9 +1011,9 @@ class AmazonConverseConfig(BaseConfig): """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None for idx, content in enumerate(content_blocks): """ - Content is either a tool response or text @@ -1133,9 +1134,9 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None if message is not None: ( @@ -1148,12 +1149,12 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message["provider_specific_fields"] = { "reasoningContentBlocks": reasoningContentBlocks, } - chat_completion_message["reasoning_content"] = ( - self._transform_reasoning_content(reasoningContentBlocks) - ) - chat_completion_message["thinking_blocks"] = ( - self._transform_thinking_blocks(reasoningContentBlocks) - ) + chat_completion_message[ + "reasoning_content" + ] = self._transform_reasoning_content(reasoningContentBlocks) + chat_completion_message[ + "thinking_blocks" + ] = self._transform_thinking_blocks(reasoningContentBlocks) chat_completion_message["content"] = content_str if ( json_mode is True @@ -1171,7 +1172,6 @@ class AmazonConverseConfig(BaseConfig): # Bedrock returns the response wrapped in a "properties" object # We need to extract the actual content from this wrapper try: - response_data = json.loads(json_mode_content_str) # If Bedrock wrapped the response in "properties", extract the content diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 831a6da93b..c7f7acf331 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -738,19 +738,24 @@ class CommonBatchFilesUtils: ) # Prepare the request data - if isinstance(data, dict): - import json - request_data = json.dumps(data) + method_upper = method.upper() + if method_upper == "GET": + # GET requests should be signed with an empty payload + request_data = "" + headers = {} else: - request_data = data - - # Prepare headers - headers = {"Content-Type": "application/json"} + if isinstance(data, dict): + import json + request_data = json.dumps(data) + else: + request_data = data + # Prepare headers for non-GET requests + headers = {"Content-Type": "application/json"} # Create AWS request and sign it sigv4 = SigV4Auth(credentials, service_name, aws_region_name) request = AWSRequest( - method=method.upper(), url=endpoint_url, data=request_data, headers=headers + method=method_upper, url=endpoint_url, data=request_data, headers=headers ) sigv4.add_auth(request) prepped = request.prepare() diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 8b925a375a..dc64fea1a3 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2520,6 +2520,95 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params_with_request, ) + def retrieve_batch( + self, + batch_id: str, + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: Optional[str], + api_key: Optional[str], + logging_obj: "LiteLLMLoggingObj", + _is_async: bool = False, + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + model: Optional[str] = None, + ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]: + """ + Retrieve a batch using provider-specific configuration. + """ + # Transform the request using provider config + transformed_request = provider_config.transform_retrieve_batch_request( + batch_id=batch_id, + optional_params=litellm_params, + litellm_params=litellm_params, + ) + + if _is_async: + return self.async_retrieve_batch( + transformed_request=transformed_request, + litellm_params=litellm_params, + provider_config=provider_config, + headers=headers, + api_base=api_base, + logging_obj=logging_obj, + client=client, + timeout=timeout, + batch_id=batch_id, + model=model, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client() + else: + sync_httpx_client = client + + try: + if ( + isinstance(transformed_request, dict) + and "method" in transformed_request + ): + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + method = transformed_request["method"].lower() + request_kwargs = { + "url": transformed_request["url"], + "headers": transformed_request["headers"], + } + + # Only add data for non-GET requests + if method != "get" and transformed_request.get("data") is not None: + request_kwargs["data"] = transformed_request["data"] + + batch_response = getattr(sync_httpx_client, method)(**request_kwargs) + elif isinstance(transformed_request, dict) and api_base: + # For other providers that use JSON requests + batch_response = sync_httpx_client.get( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + params=transformed_request, + ) + else: + # Handle other request types if needed + if not api_base: + raise ValueError("api_base is required for non-pre-signed requests") + batch_response = sync_httpx_client.get( + url=api_base, + headers=headers, + ) + except Exception as e: + verbose_logger.exception(f"Error retrieving batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + return provider_config.transform_retrieve_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params, + ) + async def async_create_batch( self, transformed_request: Union[bytes, str, dict], @@ -2606,6 +2695,89 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params_with_request, ) + async def async_retrieve_batch( + self, + transformed_request: Union[bytes, str, dict], + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: Optional[str], + logging_obj: "LiteLLMLoggingObj", + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + batch_id: Optional[str] = None, + model: Optional[str] = None, + ): + """ + Async version of retrieve_batch + """ + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=provider_config.custom_llm_provider + ) + else: + async_httpx_client = client + + ######################################################### + # Debug Logging + ######################################################### + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": transformed_request, + "api_base": api_base, + "headers": headers, + "batch_id": batch_id, + }, + ) + + try: + if ( + isinstance(transformed_request, dict) + and "method" in transformed_request + ): + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + method = transformed_request["method"].lower() + request_kwargs = { + "url": transformed_request["url"], + "headers": transformed_request["headers"], + } + + # Only add data for non-GET requests + if method != "get" and transformed_request.get("data") is not None: + request_kwargs["data"] = transformed_request["data"] + + batch_response = await getattr(async_httpx_client, method)(**request_kwargs) + elif isinstance(transformed_request, dict) and api_base: + # For other providers that use JSON requests + batch_response = await async_httpx_client.get( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + params=transformed_request, + ) + else: + # Handle other request types if needed + if not api_base: + raise ValueError("api_base is required for non-pre-signed requests") + batch_response = await async_httpx_client.get( + url=api_base, + headers=headers, + ) + except Exception as e: + verbose_logger.exception(f"Error retrieving batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + return provider_config.transform_retrieve_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params, + ) + def cancel_response_api_handler( self, response_id: str, diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 3be373ca5e..6755cab22e 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -378,7 +378,7 @@ class OCIChatConfig(BaseConfig): or not oci_compartment_id ): raise Exception( - "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, " + "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id " "and at least one of oci_key or oci_key_file." ) diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index 76998e7669..0f0bc776cc 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -239,6 +239,7 @@ class VertexBase: stream=stream, auth_header=None, url=default_api_base, + model=model, ) return api_base @@ -292,6 +293,7 @@ class VertexBase: stream: Optional[bool], auth_header: Optional[str], url: str, + model: Optional[str] = None, ) -> Tuple[Optional[str], str]: """ for cloudflare ai gateway - https://github.com/BerriAI/litellm/issues/4317 @@ -301,7 +303,12 @@ class VertexBase: """ if api_base: if custom_llm_provider == "gemini": - url = "{}:{}".format(api_base, endpoint) + # For Gemini (Google AI Studio), construct the full path like other providers + if model is None: + raise ValueError( + "Model parameter is required for Gemini custom API base URLs" + ) + url = "{}/models/{}:{}".format(api_base, model, endpoint) if gemini_api_key is None: raise ValueError( "Missing gemini_api_key, please set `GEMINI_API_KEY`" @@ -373,6 +380,7 @@ class VertexBase: endpoint=endpoint, stream=stream, url=url, + model=model, ) def _handle_reauthentication( @@ -384,19 +392,19 @@ class VertexBase: ) -> Tuple[str, str]: """ Handle reauthentication when credentials refresh fails. - + This method clears the cached credentials and attempts to reload them once. It should only be called when "Reauthentication is needed" error occurs. - + Args: credentials: The original credentials project_id: The project ID credential_cache_key: The cache key to clear error: The original error that triggered reauthentication - + Returns: Tuple of (access_token, project_id) - + Raises: The original error if reauthentication fails """ @@ -404,11 +412,11 @@ class VertexBase: f"Handling reauthentication for project_id: {project_id}. " f"Clearing cache and retrying once." ) - + # Clear the cached credentials if credential_cache_key in self._credentials_project_mapping: del self._credentials_project_mapping[credential_cache_key] - + # Retry once with _retry_reauth=True to prevent infinite recursion try: return self.get_access_token( @@ -438,12 +446,12 @@ class VertexBase: 3. Check if loaded credentials have expired 4. If expired, refresh credentials 5. Return access token and project id - + Args: credentials: The credentials to use for authentication project_id: The Google Cloud project ID _retry_reauth: Internal flag to prevent infinite recursion during reauthentication - + Returns: Tuple of (access_token, project_id) """ diff --git a/litellm/main.py b/litellm/main.py index 2f24f8b3be..6100ab5de2 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -116,6 +116,7 @@ from litellm.utils import ( from ._logging import verbose_logger from .caching.caching import disable_cache, enable_cache, update_cache +from .litellm_core_utils.core_helpers import safe_deep_copy from .litellm_core_utils.fallback_utils import ( async_completion_with_fallbacks, completion_with_fallbacks, @@ -2847,8 +2848,7 @@ def completion( # type: ignore # noqa: PLR0915 ) api_base = api_base or litellm.api_base or get_secret("GEMINI_API_BASE") - - new_params = deepcopy(optional_params) + new_params = safe_deep_copy(optional_params or {}) response = vertex_chat_completion.completion( # type: ignore model=model, messages=messages, @@ -2892,7 +2892,7 @@ def completion( # type: ignore # noqa: PLR0915 api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE") - new_params = deepcopy(optional_params) + new_params = safe_deep_copy(optional_params or {}) if vertex_partner_models_chat_completion.is_vertex_partner_model(model): model_response = vertex_partner_models_chat_completion.completion( model=model, diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 96e19d022c..5e5bccb81e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1,2570 +1,821 @@ { - "sample_spec": { - "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.", - "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens", - "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "output_cost_per_reasoning_token": 0.0, - "input_cost_per_audio_token": 0.0, - "litellm_provider": "one of https://docs.litellm.ai/docs/providers", - "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_reasoning": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.0, - "search_context_size_medium": 0.0, - "search_context_size_high": 0.0 - }, - "file_search_cost_per_1k_calls": 0.0, - "file_search_cost_per_gb_per_day": 0.0, - "vector_store_cost_per_gb_per_day": 0.0, - "computer_use_input_cost_per_1k_tokens": 0.0, - "computer_use_output_cost_per_1k_tokens": 0.0, - "code_interpreter_cost_per_session": 0.0, - "supported_regions": [ - "global", - "us-west-2", - "eu-west-1", - "ap-southeast-1", - "ap-northeast-1" - ], - "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD" - }, - "omni-moderation-latest": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-latest-intents": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-2024-09-26": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "gpt-4": { - "max_tokens": 4096, - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "input_cost_per_token": 3e-05, - "output_cost_per_token": 6e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4.1": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-mini": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 1.6e-06, - "input_cost_per_token_batches": 2e-07, - "output_cost_per_token_batches": 8e-07, - "cache_read_input_token_cost": 1e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-mini-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 1.6e-06, - "input_cost_per_token_batches": 2e-07, - "output_cost_per_token_batches": 8e-07, - "cache_read_input_token_cost": 1e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-nano": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 4e-07, - "input_cost_per_token_batches": 5e-08, - "output_cost_per_token_batches": 2e-07, - "cache_read_input_token_cost": 2.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-nano-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 4e-07, - "input_cost_per_token_batches": 5e-08, - "output_cost_per_token_batches": 2e-07, - "cache_read_input_token_cost": 2.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4o": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "watsonx/ibm/granite-3-8b-instruct": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 1024, - "input_cost_per_token": 0.0002, - "output_cost_per_token": 0.0002, - "litellm_provider": "watsonx", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_parallel_function_calling": false, - "supports_vision": false, - "supports_audio_input": false, - "supports_audio_output": false, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true - }, - "watsonx/mistralai/mistral-large": { - "max_tokens": 131072, - "max_input_tokens": 131072, - "max_output_tokens": 16384, - "input_cost_per_token": 3e-06, - "output_cost_per_token": 1e-05, - "litellm_provider": "watsonx", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_parallel_function_calling": false, - "supports_vision": false, - "supports_audio_input": false, - "supports_audio_output": false, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true - }, - "gpt-4o-search-preview-2025-03-11": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-search-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.03, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.05 - } - }, - "gpt-4.5-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 7.5e-05, - "output_cost_per_token": 0.00015, - "input_cost_per_token_batches": 3.75e-05, - "output_cost_per_token_batches": 7.5e-05, - "cache_read_input_token_cost": 3.75e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4.5-preview-2025-02-27": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 7.5e-05, - "output_cost_per_token": 0.00015, - "input_cost_per_token_batches": 3.75e-05, - "output_cost_per_token_batches": 7.5e-05, - "cache_read_input_token_cost": 3.75e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - 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Defaulting to base model pricing", + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4o-2024-08-06": { + "input_cost_per_token": 3.75e-06, + "input_cost_per_token_batches": 1.875e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_batches": 7.5e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-2024-11-20": { + "cache_creation_input_token_cost": 1.875e-06, + "input_cost_per_token": 3.75e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-mini-2024-07-18": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "output_cost_per_token_batches": 6e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.0-pro": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 32760, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, "gemini-1.0-pro-001": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", + "max_tokens": 8192, "mode": "chat", - "supports_function_calling": true, + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.0-ultra": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", "supports_function_calling": true, - "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.0-ultra-001": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-002": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, + "max_tokens": 8192, + "mode": "chat", "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - 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"supports_pdf_input": true, - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-002": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 1.25e-06, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 2.5e-06, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 5e-06, - "output_cost_per_character": 1.25e-06, - 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"input_cost_per_token_above_128k_tokens": 2.5e-06, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 5e-06, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 1e-05, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_vision": true, - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-05-24", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-preview-0514": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 7.8125e-08, - 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"input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 7.8125e-08, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 1.5625e-07, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 3.125e-07, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 6.25e-07, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - 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"output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-exp-0827": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 4.688e-09, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 4.6875e-09, - "output_cost_per_character": 1.875e-08, - "output_cost_per_token_above_128k_tokens": 9.375e-09, - "output_cost_per_character_above_128k_tokens": 3.75e-08, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-002": { - "max_tokens": 8192, - "max_input_tokens": 1048576, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 3e-07, - "output_cost_per_character": 7.5e-08, - "output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-1.5-flash", - "deprecation_date": "2025-09-24", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-001": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 3e-07, - "output_cost_per_character": 7.5e-08, - "output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-05-24", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-preview-0514": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 4.6875e-09, - "output_cost_per_character": 1.875e-08, - "output_cost_per_token_above_128k_tokens": 9.375e-09, - "output_cost_per_character_above_128k_tokens": 3.75e-08, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-pro-experimental": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_token": 0, - "output_cost_per_token": 0, - "input_cost_per_character": 0, - "output_cost_per_character": 0, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": false, - "supports_tool_choice": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental", - "supports_parallel_function_calling": true - }, - "gemini-flash-experimental": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_token": 0, - "output_cost_per_token": 0, - "input_cost_per_character": 0, - "output_cost_per_character": 0, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": false, - "supports_tool_choice": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental", - "supports_parallel_function_calling": true - }, - "gemini-pro-vision": { - "max_tokens": 2048, - "max_input_tokens": 16384, - "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, - "max_video_length": 2, - "input_cost_per_token": 5e-07, "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-vision": { - "max_tokens": 2048, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, - "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", + "max_videos_per_prompt": 1, "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, "gemini-1.0-pro-vision-001": { - "max_tokens": 2048, + "deprecation_date": "2025-04-09", + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, + "max_videos_per_prompt": 1, + "mode": "chat", "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", "supports_function_calling": true, - "supports_vision": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, - "medlm-medium": { - "max_tokens": 8192, - "max_input_tokens": 32768, - "max_output_tokens": 8192, - "input_cost_per_character": 5e-07, - "output_cost_per_character": 1e-06, + "gemini-1.0-ultra": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true - }, - "medlm-large": { - "max_tokens": 1024, "max_input_tokens": 8192, - "max_output_tokens": 1024, - "input_cost_per_character": 5e-06, - "output_cost_per_character": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", + "max_output_tokens": 2048, + "max_tokens": 8192, "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "gemini-2.5-pro-exp-03-25": { - "max_tokens": 65535, - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, + "gemini-1.0-ultra-001": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 8192, + "max_output_tokens": 2048, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true + }, + "gemini-1.5-flash": { + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", "max_audio_length_hours": 8.4, "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, "max_pdf_size_mb": 30, - "input_cost_per_token": 1.25e-06, - "input_cost_per_token_above_200k_tokens": 2.5e-06, - "output_cost_per_token": 1e-05, - "output_cost_per_token_above_200k_tokens": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_video_input": true, - "supports_pdf_input": true, - "supports_response_schema": true, - "supports_tool_choice": true, - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" - ], - "supported_modalities": [ - "text", - "image", - "audio", - "video" - ], - "supported_output_modalities": [ - "text" - ], - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supports_parallel_function_calling": true, - "supports_web_search": true, - "cache_read_input_token_cost": 3.125e-07, - "supports_prompt_caching": true - }, - "gemini-2.0-pro-exp-02-05": { "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_character": 7.5e-08, + "output_cost_per_character_above_128k_tokens": 1.5e-07, + "output_cost_per_token": 3e-07, + "output_cost_per_token_above_128k_tokens": 6e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.5-flash-001": { + "deprecation_date": "2025-05-24", + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + 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true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "tpm": 4000000, - "rpm": 1000, - "source": "https://ai.google.dev/pricing", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 8192, + "max_pdf_size_mb": 30, + "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, "metadata": { "notes": "Rate limits not documented for gemini-exp-1114. Assuming same as gemini-1.5-pro.", "supports_tool_choice": true - } - }, - "gemini/gemini-exp-1206": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_token": 0, - "input_cost_per_token_above_128k_tokens": 0, + }, + "mode": "chat", "output_cost_per_token": 0, "output_cost_per_token_above_128k_tokens": 0, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_response_schema": true, - "tpm": 4000000, "rpm": 1000, "source": "https://ai.google.dev/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "tpm": 4000000 + }, + "gemini/gemini-exp-1206": { + "input_cost_per_token": 0, + "input_cost_per_token_above_128k_tokens": 0, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 2097152, + "max_output_tokens": 8192, + "max_pdf_size_mb": 30, + "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, "metadata": { "notes": "Rate limits not documented for gemini-exp-1206. Assuming same as gemini-1.5-pro.", "supports_tool_choice": true - } - }, - "gemini/gemini-1.5-flash-exp-0827": { - "max_tokens": 8192, - "max_input_tokens": 1048576, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_token": 0, - "input_cost_per_token_above_128k_tokens": 0, + }, + "mode": "chat", "output_cost_per_token": 0, "output_cost_per_token_above_128k_tokens": 0, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "tpm": 4000000, - "rpm": 2000, - "source": "https://ai.google.dev/pricing", - "supports_tool_choice": true - }, - "gemini/gemini-1.5-flash-8b-exp-0827": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_token": 0, - "input_cost_per_token_above_128k_tokens": 0, - "output_cost_per_token": 0, - "output_cost_per_token_above_128k_tokens": 0, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "tpm": 4000000, - "rpm": 4000, - "source": "https://ai.google.dev/pricing", - "supports_tool_choice": true - }, - "gemini/gemini-pro": { - "max_tokens": 8192, - "max_input_tokens": 32760, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-07, - "input_cost_per_token_above_128k_tokens": 7e-07, - "output_cost_per_token": 1.05e-06, - "output_cost_per_token_above_128k_tokens": 2.1e-06, - "litellm_provider": "gemini", - "mode": "chat", - "supports_function_calling": true, - "rpd": 30000, - "tpm": 120000, - "rpm": 360, - "source": "https://ai.google.dev/gemini-api/docs/models/gemini", - "supports_tool_choice": true - }, - "gemini/gemini-1.5-pro": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-06, - "input_cost_per_token_above_128k_tokens": 7e-06, - "output_cost_per_token": 1.05e-05, - "output_cost_per_token_above_128k_tokens": 2.1e-05, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "tpm": 4000000, - "rpm": 1000, - "source": "https://ai.google.dev/pricing" - }, - "gemini/gemini-1.5-pro-002": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-06, - "input_cost_per_token_above_128k_tokens": 7e-06, - "output_cost_per_token": 1.05e-05, - "output_cost_per_token_above_128k_tokens": 2.1e-05, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "supports_prompt_caching": true, - "tpm": 4000000, "rpm": 1000, "source": "https://ai.google.dev/pricing", - "deprecation_date": "2025-09-24" - }, - "gemini/gemini-1.5-pro-001": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-06, - "input_cost_per_token_above_128k_tokens": 7e-06, - "output_cost_per_token": 1.05e-05, - "output_cost_per_token_above_128k_tokens": 2.1e-05, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, "supports_function_calling": true, - "supports_vision": true, - "supports_tool_choice": true, "supports_response_schema": true, - "supports_prompt_caching": true, - "tpm": 4000000, - "rpm": 1000, - "source": "https://ai.google.dev/pricing", - "deprecation_date": "2025-05-24" - }, - "gemini/gemini-1.5-pro-exp-0801": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-06, - "input_cost_per_token_above_128k_tokens": 7e-06, - "output_cost_per_token": 1.05e-05, - "output_cost_per_token_above_128k_tokens": 2.1e-05, - "litellm_provider": "gemini", - "mode": "chat", "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, "supports_tool_choice": true, - "supports_response_schema": true, - "tpm": 4000000, - "rpm": 1000, - "source": "https://ai.google.dev/pricing" - }, - "gemini/gemini-1.5-pro-exp-0827": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_token": 0, - "input_cost_per_token_above_128k_tokens": 0, - "output_cost_per_token": 0, - "output_cost_per_token_above_128k_tokens": 0, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, "supports_vision": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "tpm": 4000000, - "rpm": 1000, - "source": "https://ai.google.dev/pricing" - }, - "gemini/gemini-1.5-pro-latest": { - "max_tokens": 8192, - "max_input_tokens": 1048576, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-06, - "input_cost_per_token_above_128k_tokens": 7e-06, - "output_cost_per_token": 1.05e-06, - "output_cost_per_token_above_128k_tokens": 2.1e-05, - "litellm_provider": "gemini", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "tpm": 4000000, - "rpm": 1000, - "source": "https://ai.google.dev/pricing" - }, - "gemini/gemini-pro-vision": { - "max_tokens": 2048, - "max_input_tokens": 30720, - "max_output_tokens": 2048, - "input_cost_per_token": 3.5e-07, - "input_cost_per_token_above_128k_tokens": 7e-07, - "output_cost_per_token": 1.05e-06, - "output_cost_per_token_above_128k_tokens": 2.1e-06, - "litellm_provider": "gemini", - "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, - "rpd": 30000, - "tpm": 120000, - "rpm": 360, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true + "tpm": 4000000 }, "gemini/gemini-gemma-2-27b-it": { - "max_tokens": 8192, - "max_output_tokens": 8192, "input_cost_per_token": 3.5e-07, - "output_cost_per_token": 1.05e-06, "litellm_provider": "gemini", + "max_output_tokens": 8192, + "max_tokens": 8192, "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, + "output_cost_per_token": 1.05e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true }, "gemini/gemini-gemma-2-9b-it": { - "max_tokens": 8192, - "max_output_tokens": 8192, "input_cost_per_token": 3.5e-07, - "output_cost_per_token": 1.05e-06, "litellm_provider": "gemini", + "max_output_tokens": 8192, + "max_tokens": 8192, "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, + "output_cost_per_token": 1.05e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true }, - "gemini/imagen-4.0-generate-001": { - "output_cost_per_image": 0.04, + "gemini/gemini-pro": { + "input_cost_per_token": 3.5e-07, + "input_cost_per_token_above_128k_tokens": 7e-07, + "litellm_provider": "gemini", + "max_input_tokens": 32760, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.05e-06, + "output_cost_per_token_above_128k_tokens": 2.1e-06, + "rpd": 30000, + "rpm": 360, + "source": "https://ai.google.dev/gemini-api/docs/models/gemini", + "supports_function_calling": true, + "supports_tool_choice": true, + "tpm": 120000 + }, + "gemini/gemini-pro-vision": { + "input_cost_per_token": 3.5e-07, + "input_cost_per_token_above_128k_tokens": 7e-07, + "litellm_provider": "gemini", + "max_input_tokens": 30720, + "max_output_tokens": 2048, + "max_tokens": 2048, + "mode": "chat", + "output_cost_per_token": 1.05e-06, + "output_cost_per_token_above_128k_tokens": 2.1e-06, + "rpd": 30000, + "rpm": 360, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "tpm": 120000 + }, + "gemini/gemma-3-27b-it": { + "input_cost_per_audio_per_second": 0, + "input_cost_per_audio_per_second_above_128k_tokens": 0, + "input_cost_per_character": 0, + "input_cost_per_character_above_128k_tokens": 0, + "input_cost_per_image": 0, + "input_cost_per_image_above_128k_tokens": 0, + "input_cost_per_token": 0, + "input_cost_per_token_above_128k_tokens": 0, + "input_cost_per_video_per_second": 0, + "input_cost_per_video_per_second_above_128k_tokens": 0, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 0, + "output_cost_per_character_above_128k_tokens": 0, + "output_cost_per_token": 0, + "output_cost_per_token_above_128k_tokens": 0, + "source": "https://aistudio.google.com", + "supports_audio_output": false, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini/imagen-3.0-fast-generate-001": { "litellm_provider": "gemini", "mode": "image_generation", - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" - }, - "gemini/imagen-4.0-ultra-generate-001": { - "output_cost_per_image": 0.06, - "litellm_provider": "gemini", - "mode": "image_generation", - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" - }, - "gemini/imagen-4.0-fast-generate-001": { "output_cost_per_image": 0.02, - "litellm_provider": "gemini", - "mode": "image_generation", - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" - }, - "gemini/imagen-3.0-generate-002": { - "output_cost_per_image": 0.04, - "litellm_provider": "gemini", - "mode": "image_generation", "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "gemini/imagen-3.0-generate-001": { - "output_cost_per_image": 0.04, "litellm_provider": "gemini", "mode": "image_generation", + "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, - "gemini/imagen-3.0-fast-generate-001": { + "gemini/imagen-3.0-generate-002": { + "litellm_provider": "gemini", + "mode": "image_generation", + "output_cost_per_image": 0.04, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" + }, + "gemini/imagen-4.0-fast-generate-001": { + "litellm_provider": "gemini", + "mode": "image_generation", "output_cost_per_image": 0.02, - "litellm_provider": "gemini", - "mode": "image_generation", "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, - "command-a-03-2025": { - "max_tokens": 8000, - "max_input_tokens": 256000, - "max_output_tokens": 8000, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "litellm_provider": "cohere_chat", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true - }, - "command-r": { - "max_tokens": 4096, - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 6e-07, - "litellm_provider": "cohere_chat", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true - }, - "command-r-08-2024": { - "max_tokens": 4096, - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 6e-07, - "litellm_provider": "cohere_chat", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true - }, - "command-r7b-12-2024": { - "max_tokens": 4096, - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 3.75e-08, - "litellm_provider": "cohere_chat", - "mode": "chat", - "supports_function_calling": true, - "source": "https://docs.cohere.com/v2/docs/command-r7b", - "supports_tool_choice": true - }, - "command-light": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 3e-07, - "output_cost_per_token": 6e-07, - "litellm_provider": "cohere_chat", - "mode": "chat", - "supports_tool_choice": true - }, - "command-r-plus": { - "max_tokens": 4096, - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "litellm_provider": "cohere_chat", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true - }, - "command-r-plus-08-2024": { - "max_tokens": 4096, - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "litellm_provider": "cohere_chat", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true - }, - "command-nightly": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1e-06, - "output_cost_per_token": 2e-06, - "litellm_provider": "cohere", - "mode": "completion" - }, - "command": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1e-06, - "output_cost_per_token": 2e-06, - "litellm_provider": "cohere", - "mode": "completion" - }, - "rerank-v3.5": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "max_query_tokens": 2048, - "input_cost_per_token": 0.0, - "input_cost_per_query": 0.002, - "output_cost_per_token": 0.0, - "litellm_provider": "cohere", - "mode": "rerank" - }, - "rerank-english-v3.0": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "max_query_tokens": 2048, - "input_cost_per_token": 0.0, - "input_cost_per_query": 0.002, - "output_cost_per_token": 0.0, - "litellm_provider": "cohere", - "mode": "rerank" - }, - "rerank-multilingual-v3.0": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "max_query_tokens": 2048, - "input_cost_per_token": 0.0, - "input_cost_per_query": 0.002, - "output_cost_per_token": 0.0, - "litellm_provider": "cohere", - "mode": "rerank" - }, - "rerank-english-v2.0": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "max_query_tokens": 2048, - "input_cost_per_token": 0.0, - "input_cost_per_query": 0.002, - "output_cost_per_token": 0.0, - "litellm_provider": "cohere", - "mode": "rerank" - }, - "rerank-multilingual-v2.0": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "max_query_tokens": 2048, - "input_cost_per_token": 0.0, - "input_cost_per_query": 0.002, - 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"input_cost_per_second": 8.667e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0052, - "calculation": "$0.0052/60 seconds = $0.00008667 per second (multilingual)" - } - }, - "deepgram/nova-2": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-voicemail": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-conversationalai": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-video": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-drivethru": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-automotive": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-atc": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/enhanced": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-general": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - 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"sambanova/Meta-Llama-3.3-70B-Instruct": { + "input_cost_per_token": 6e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-Guard-3-8B": { + "input_cost_per_token": 3e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 3e-07, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/QwQ-32B": { + "input_cost_per_token": 5e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1e-06, + "source": "https://cloud.sambanova.ai/plans/pricing" + 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"max_tokens": 128000, + "mode": "chat" + }, + "snowflake/mixtral-8x7b": { + "litellm_provider": "snowflake", + "max_input_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 32000, + "mode": "chat" + }, + "snowflake/reka-core": { + "litellm_provider": "snowflake", + "max_input_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 32000, + "mode": "chat" + }, + "snowflake/reka-flash": { + "litellm_provider": "snowflake", + "max_input_tokens": 100000, + "max_output_tokens": 8192, + "max_tokens": 100000, + "mode": "chat" + }, + "snowflake/snowflake-arctic": { + "litellm_provider": "snowflake", + "max_input_tokens": 4096, + "max_output_tokens": 8192, + "max_tokens": 4096, + "mode": "chat" + }, + "snowflake/snowflake-llama-3.1-405b": { + "litellm_provider": "snowflake", + "max_input_tokens": 8000, + "max_output_tokens": 8192, + "max_tokens": 8000, + "mode": "chat" + }, + "snowflake/snowflake-llama-3.3-70b": { + "litellm_provider": "snowflake", + "max_input_tokens": 8000, + "max_output_tokens": 8192, + "max_tokens": 8000, + "mode": "chat" + }, + "stability.sd3-5-large-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.08 + }, + "stability.sd3-large-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.08 + }, + "stability.stable-image-core-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.04 + }, + "stability.stable-image-core-v1:1": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.04 + }, + "stability.stable-image-ultra-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.14 + }, + "stability.stable-image-ultra-v1:1": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.14 + }, + "standard/1024-x-1024/dall-e-3": { + "input_cost_per_pixel": 3.81469e-08, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_pixel": 0.0 + }, + "standard/1024-x-1792/dall-e-3": { + "input_cost_per_pixel": 4.359e-08, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_pixel": 0.0 + }, + "standard/1792-x-1024/dall-e-3": { + "input_cost_per_pixel": 4.359e-08, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_pixel": 0.0 + }, + "text-bison": { + "input_cost_per_character": 2.5e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 2048, + "max_tokens": 2048, + "mode": "completion", + "output_cost_per_character": 5e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison32k": { + "input_cost_per_character": 2.5e-07, + "input_cost_per_token": 1.25e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "output_cost_per_token": 1.25e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison32k@002": { + "input_cost_per_character": 2.5e-07, + "input_cost_per_token": 1.25e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "output_cost_per_token": 1.25e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison@001": { + "input_cost_per_character": 2.5e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison@002": { + "input_cost_per_character": 2.5e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-completion-codestral/codestral-2405": { + "input_cost_per_token": 0.0, + "litellm_provider": "text-completion-codestral", + "max_input_tokens": 32000, + "max_output_tokens": 8191, + "max_tokens": 8191, + "mode": "completion", + "output_cost_per_token": 0.0, + "source": "https://docs.mistral.ai/capabilities/code_generation/" + }, + 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"https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models" + }, + "text-embedding-3-large": { + "input_cost_per_token": 1.3e-07, + "input_cost_per_token_batches": 6.5e-08, + "litellm_provider": "openai", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_cost_per_token_batches": 0.0, + "output_vector_size": 3072 + }, + "text-embedding-3-small": { + "input_cost_per_token": 2e-08, + "input_cost_per_token_batches": 1e-08, + "litellm_provider": "openai", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_cost_per_token_batches": 0.0, + "output_vector_size": 1536 + }, + "text-embedding-ada-002": { + "input_cost_per_token": 1e-07, + "litellm_provider": "openai", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_vector_size": 1536 + }, + "text-embedding-ada-002-v2": { + "input_cost_per_token": 1e-07, + "input_cost_per_token_batches": 5e-08, + "litellm_provider": "openai", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_cost_per_token_batches": 0.0 + }, + "text-embedding-large-exp-03-07": { + "input_cost_per_character": 2.5e-08, + "input_cost_per_token": 1e-07, + "litellm_provider": "vertex_ai-embedding-models", + "max_input_tokens": 8192, + "max_tokens": 8192, + "mode": "embedding", + "output_cost_per_token": 0, + "output_vector_size": 3072, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models" + }, + "text-embedding-preview-0409": { + "input_cost_per_token": 6.25e-09, + "input_cost_per_token_batch_requests": 5e-09, + "litellm_provider": "vertex_ai-embedding-models", + "max_input_tokens": 3072, + "max_tokens": 3072, + "mode": "embedding", + "output_cost_per_token": 0, + "output_vector_size": 768, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" + }, + 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"max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_web_search": true + }, + "xai/grok-4-latest": { + "input_cost_per_token": 3e-06, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_web_search": true + }, + "xai/grok-beta": { + "input_cost_per_token": 5e-06, + "litellm_provider": "xai", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "xai/grok-code-fast": { + "cache_read_input_token_cost": 2e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "xai/grok-code-fast-1": { + "cache_read_input_token_cost": 2e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "xai/grok-code-fast-1-0825": { + "cache_read_input_token_cost": 2e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "xai/grok-vision-beta": { + "input_cost_per_image": 5e-06, + "input_cost_per_token": 5e-06, + "litellm_provider": "xai", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true } -} +} \ No newline at end of file diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index be9e494042..8a138b2a80 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -469,16 +469,13 @@ async def get_end_user_object( # check if in cache cached_user_obj = await user_api_key_cache.async_get_cache(key=_key) if cached_user_obj is not None: - if isinstance(cached_user_obj, dict): - return_obj = LiteLLM_EndUserTable(**cached_user_obj) - check_in_budget(end_user_obj=return_obj) - return return_obj - elif isinstance(cached_user_obj, LiteLLM_EndUserTable): - return_obj = cached_user_obj - check_in_budget(end_user_obj=return_obj) - return return_obj + # Convert cached dict to LiteLLM_EndUserTable instance + return_obj = LiteLLM_EndUserTable(**cached_user_obj) + check_in_budget(end_user_obj=return_obj) + return return_obj + # else, check db - try: + try: response = await prisma_client.db.litellm_endusertable.find_unique( where={"user_id": end_user_id}, include={"litellm_budget_table": True}, @@ -487,9 +484,9 @@ async def get_end_user_object( if response is None: raise Exception - # save the end-user object to cache + # save the end-user object to cache (always store as dict for consistency) await user_api_key_cache.async_set_cache( - key="end_user_id:{}".format(end_user_id), value=response + key="end_user_id:{}".format(end_user_id), value=response.dict() ) _response = LiteLLM_EndUserTable(**response.dict()) diff --git a/litellm/proxy/common_utils/performance_utils.py b/litellm/proxy/common_utils/performance_utils.py new file mode 100644 index 0000000000..fe238f2e33 --- /dev/null +++ b/litellm/proxy/common_utils/performance_utils.py @@ -0,0 +1,125 @@ +""" +Performance utilities for LiteLLM proxy server. + +This module provides performance monitoring and profiling functionality for endpoint +performance analysis using cProfile with configurable sampling rates. +""" + +import asyncio +import cProfile +import functools +import threading +from pathlib import Path as PathLib + +from litellm._logging import verbose_proxy_logger + +# Global profiling state +_profile_lock = threading.Lock() +_profiler = None +_last_profile_file_path = None +_sample_counter = 0 +_sample_counter_lock = threading.Lock() + + +def _should_sample(profile_sampling_rate: float) -> bool: + """Determine if current request should be sampled based on sampling rate.""" + if profile_sampling_rate >= 1.0: + return True # Always sample + elif profile_sampling_rate <= 0.0: + return False # Never sample + + # Use deterministic sampling based on counter for consistent rate + global _sample_counter + with _sample_counter_lock: + _sample_counter += 1 + # Sample based on rate (e.g., 0.1 means sample every 10th request) + should_sample = (_sample_counter % int(1.0 / profile_sampling_rate)) == 0 + return should_sample + + +def _start_profiling(profile_sampling_rate: float) -> None: + """Start cProfile profiling once globally.""" + global _profiler + with _profile_lock: + if _profiler is None: + _profiler = cProfile.Profile() + _profiler.enable() + verbose_proxy_logger.info(f"Profiling started with sampling rate: {profile_sampling_rate}") + + +def _start_profiling_for_request(profile_sampling_rate: float) -> bool: + """Start profiling for a specific request (if sampling allows).""" + if _should_sample(profile_sampling_rate): + _start_profiling(profile_sampling_rate) + return True + return False + + +def _save_stats(profile_file: PathLib) -> None: + """Save current stats directly to file.""" + with _profile_lock: + if _profiler is None: + return + try: + # Disable profiler temporarily to dump stats + _profiler.disable() + _profiler.dump_stats(str(profile_file)) + # Re-enable profiler to continue profiling + _profiler.enable() + verbose_proxy_logger.debug(f"Profiling stats saved to {profile_file}") + except Exception as e: + verbose_proxy_logger.error(f"Error saving profiling stats: {e}") + # Make sure profiler is re-enabled even if there's an error + try: + _profiler.enable() + except Exception: + pass + + +def profile_endpoint(sampling_rate: float = 1.0): + """Decorator to sample endpoint hits and save to a profile file. + + Args: + sampling_rate: Rate of requests to profile (0.0 to 1.0) + - 1.0: Profile all requests (100%) + - 0.1: Profile 1 in 10 requests (10%) + - 0.0: Profile no requests (0%) + """ + def decorator(func): + def set_last_profile_path(path: PathLib) -> None: + global _last_profile_file_path + _last_profile_file_path = path + + if asyncio.iscoroutinefunction(func): + @functools.wraps(func) + async def async_wrapper(*args, **kwargs): + is_sampling = _start_profiling_for_request(sampling_rate) + file_path_obj = PathLib("endpoint_profile.pstat") + set_last_profile_path(file_path_obj) + try: + result = await func(*args, **kwargs) + if is_sampling: + _save_stats(file_path_obj) + return result + except Exception: + if is_sampling: + _save_stats(file_path_obj) + raise + return async_wrapper + else: + @functools.wraps(func) + def sync_wrapper(*args, **kwargs): + is_sampling = _start_profiling_for_request(sampling_rate) + file_path_obj = PathLib("endpoint_profile.pstat") + set_last_profile_path(file_path_obj) + try: + result = func(*args, **kwargs) + if is_sampling: + _save_stats(file_path_obj) + return result + except Exception: + if is_sampling: + _save_stats(file_path_obj) + raise + return sync_wrapper + return decorator diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index a95f44eeca..87f4236b5a 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -832,7 +832,8 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): litellm_parent_otel_span: Union[Span, None] = ( _get_parent_otel_span_from_kwargs(kwargs) ) - user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key") + litellm_metadata = kwargs["litellm_params"]["metadata"] + user_api_key = litellm_metadata.get("user_api_key") if litellm_metadata else None pipeline_operations: List[RedisPipelineIncrementOperation] = [] if user_api_key: diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index 2ed3aed9bf..4d885d92ad 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -169,12 +169,12 @@ def _get_dynamic_logging_metadata( user_api_key_dict: UserAPIKeyAuth, proxy_config: ProxyConfig ) -> Optional[TeamCallbackMetadata]: callback_settings_obj: Optional[TeamCallbackMetadata] = None - key_dynamic_logging_settings: Optional[dict] = ( - KeyAndTeamLoggingSettings.get_key_dynamic_logging_settings(user_api_key_dict) - ) - team_dynamic_logging_settings: Optional[dict] = ( - KeyAndTeamLoggingSettings.get_team_dynamic_logging_settings(user_api_key_dict) - ) + key_dynamic_logging_settings: Optional[ + dict + ] = KeyAndTeamLoggingSettings.get_key_dynamic_logging_settings(user_api_key_dict) + team_dynamic_logging_settings: Optional[ + dict + ] = KeyAndTeamLoggingSettings.get_team_dynamic_logging_settings(user_api_key_dict) ######################################################################################### # Key-based callbacks ######################################################################################### @@ -562,6 +562,8 @@ class LiteLLMProxyRequestSetup: user_api_key_logged_metadata = StandardLoggingUserAPIKeyMetadata( user_api_key_hash=user_api_key_dict.api_key, # just the hashed token user_api_key_alias=user_api_key_dict.key_alias, + user_api_key_spend=user_api_key_dict.spend, + user_api_key_max_budget=user_api_key_dict.max_budget, user_api_key_team_id=user_api_key_dict.team_id, user_api_key_user_id=user_api_key_dict.user_id, user_api_key_org_id=user_api_key_dict.org_id, @@ -569,6 +571,7 @@ class LiteLLMProxyRequestSetup: user_api_key_end_user_id=user_api_key_dict.end_user_id, user_api_key_user_email=user_api_key_dict.user_email, user_api_key_request_route=user_api_key_dict.request_route, + user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at, ) return user_api_key_logged_metadata @@ -611,11 +614,11 @@ class LiteLLMProxyRequestSetup: ## KEY-LEVEL SPEND LOGS / TAGS if "tags" in key_metadata and key_metadata["tags"] is not None: - data[_metadata_variable_name]["tags"] = ( - LiteLLMProxyRequestSetup._merge_tags( - request_tags=data[_metadata_variable_name].get("tags"), - tags_to_add=key_metadata["tags"], - ) + data[_metadata_variable_name][ + "tags" + ] = LiteLLMProxyRequestSetup._merge_tags( + request_tags=data[_metadata_variable_name].get("tags"), + tags_to_add=key_metadata["tags"], ) if "spend_logs_metadata" in key_metadata and isinstance( key_metadata["spend_logs_metadata"], dict @@ -844,9 +847,9 @@ async def add_litellm_data_to_request( # noqa: PLR0915 data[_metadata_variable_name]["litellm_api_version"] = version if general_settings is not None: - data[_metadata_variable_name]["global_max_parallel_requests"] = ( - general_settings.get("global_max_parallel_requests", None) - ) + data[_metadata_variable_name][ + "global_max_parallel_requests" + ] = general_settings.get("global_max_parallel_requests", None) ### KEY-LEVEL Controls key_metadata = user_api_key_dict.metadata diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 6e5d4faabd..f16bae559b 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -474,6 +474,9 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): user_api_key_team_alias=user_api_key_dict.team_alias, user_api_key_end_user_id=user_api_key_dict.end_user_id, user_api_key_request_route=user_api_key_dict.request_route, + user_api_key_spend=user_api_key_dict.spend, + user_api_key_max_budget=user_api_key_dict.max_budget, + user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at, ) ) @@ -1003,7 +1006,7 @@ class InitPassThroughEndpointHelpers: ): """Add exact path route for pass-through endpoint""" route_key = f"{endpoint_id}:exact:{path}" - + # Check if this exact route is already registered if route_key in _registered_pass_through_routes: verbose_proxy_logger.debug( @@ -1011,7 +1014,7 @@ class InitPassThroughEndpointHelpers: path, ) return - + verbose_proxy_logger.debug( "adding exact pass through endpoint: %s, dependencies: %s", path, @@ -1032,12 +1035,12 @@ class InitPassThroughEndpointHelpers: methods=["GET", "POST", "PUT", "DELETE", "PATCH"], dependencies=dependencies, ) - + # Register the route to prevent duplicates _registered_pass_through_routes[route_key] = { "endpoint_id": endpoint_id, "path": path, - "type": "exact" + "type": "exact", } @staticmethod @@ -1055,7 +1058,7 @@ class InitPassThroughEndpointHelpers: """Add wildcard route for sub-paths""" wildcard_path = f"{path}/{{subpath:path}}" route_key = f"{endpoint_id}:subpath:{path}" - + # Check if this subpath route is already registered if route_key in _registered_pass_through_routes: verbose_proxy_logger.debug( @@ -1063,7 +1066,7 @@ class InitPassThroughEndpointHelpers: wildcard_path, ) return - + verbose_proxy_logger.debug( "adding wildcard pass through endpoint: %s, dependencies: %s", wildcard_path, @@ -1085,19 +1088,20 @@ class InitPassThroughEndpointHelpers: methods=["GET", "POST", "PUT", "DELETE", "PATCH"], dependencies=dependencies, ) - + # Register the route to prevent duplicates _registered_pass_through_routes[route_key] = { "endpoint_id": endpoint_id, "path": path, - "type": "subpath" + "type": "subpath", } @staticmethod def remove_endpoint_routes(endpoint_id: str): """Remove all routes for a specific endpoint ID from the registry""" keys_to_remove = [ - key for key, value in _registered_pass_through_routes.items() + key + for key, value in _registered_pass_through_routes.items() if value["endpoint_id"] == endpoint_id ] for key in keys_to_remove: @@ -1480,7 +1484,7 @@ async def delete_pass_through_endpoints( pass_through_endpoint_data.pop(endpoint_index) response_obj = found_endpoint - # Remove routes from registry + # Remove routes from registry InitPassThroughEndpointHelpers.remove_endpoint_routes(endpoint_id) ## Update db diff --git a/litellm/responses/litellm_completion_transformation/session_handler.py b/litellm/responses/litellm_completion_transformation/session_handler.py index bfb996a238..d1e009e62d 100644 --- a/litellm/responses/litellm_completion_transformation/session_handler.py +++ b/litellm/responses/litellm_completion_transformation/session_handler.py @@ -141,7 +141,7 @@ class ResponsesSessionHandler: # Add Output messages for this Spend Log ############################################################ _response_output = spend_log.get("response", "{}") - if isinstance(_response_output, dict): + if isinstance(_response_output, dict) and _response_output and _response_output != {}: # transform `ChatCompletion Response` to `ResponsesAPIResponse` model_response = ModelResponse(**_response_output) for choice in model_response.choices: diff --git a/litellm/types/integrations/datadog_llm_obs.py b/litellm/types/integrations/datadog_llm_obs.py index 25ee0932b1..1f281e93e8 100644 --- a/litellm/types/integrations/datadog_llm_obs.py +++ b/litellm/types/integrations/datadog_llm_obs.py @@ -83,3 +83,10 @@ class DDLLMObsLatencyMetrics(TypedDict, total=False): time_to_first_token_ms: float litellm_overhead_time_ms: float guardrail_overhead_time_ms: float + + +class DDLLMObsSpendMetrics(TypedDict, total=False): + response_cost: float + user_api_key_spend: float + user_api_key_max_budget: float + user_api_key_budget_reset_at: str diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py index baa7c20520..a829a6b94b 100644 --- a/litellm/types/llms/bedrock.py +++ b/litellm/types/llms/bedrock.py @@ -3,14 +3,9 @@ from typing import Any, List, Literal, Optional, Union from typing_extensions import ( TYPE_CHECKING, - Protocol, Required, - Self, TypedDict, - TypeGuard, - get_origin, override, - runtime_checkable, ) from .openai import ChatCompletionToolCallChunk @@ -93,6 +88,12 @@ class BedrockConverseReasoningContentBlockDelta(TypedDict, total=False): text: str +class GuardrailConverseContentBlock(TypedDict, total=False): + """Content block for selective guardrail evaluation in Bedrock Converse API""" + + text: str + + class ContentBlock(TypedDict, total=False): text: str image: ImageBlock @@ -102,6 +103,7 @@ class ContentBlock(TypedDict, total=False): toolUse: ToolUseBlock cachePoint: CachePointBlock reasoningContent: BedrockConverseReasoningContentBlock + guardrailConverseContent: GuardrailConverseContentBlock class MessageBlock(TypedDict): @@ -581,30 +583,35 @@ class AmazonDeepSeekR1StreamingResponse(TypedDict): class BedrockS3InputDataConfig(TypedDict): """S3 input data configuration for Bedrock batch jobs.""" + s3Uri: str class BedrockInputDataConfig(TypedDict): """Input data configuration for Bedrock batch jobs.""" + s3InputDataConfig: BedrockS3InputDataConfig class BedrockS3OutputDataConfig(TypedDict): """S3 output data configuration for Bedrock batch jobs.""" + s3Uri: str class BedrockOutputDataConfig(TypedDict): """Output data configuration for Bedrock batch jobs.""" + s3OutputDataConfig: BedrockS3OutputDataConfig class BedrockCreateBatchRequest(TypedDict, total=False): """ Request structure for creating a Bedrock batch inference job. - + Reference: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelInvocationJob.html """ + jobName: str roleArn: str modelId: str @@ -616,21 +623,17 @@ class BedrockCreateBatchRequest(TypedDict, total=False): BedrockBatchJobStatus = Literal[ - "Submitted", - "InProgress", - "Completed", - "Failed", - "Stopping", - "Stopped" + "Submitted", "InProgress", "Completed", "Failed", "Stopping", "Stopped" ] class BedrockCreateBatchResponse(TypedDict): """ Response structure from creating a Bedrock batch inference job. - + Reference: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelInvocationJob.html """ + jobArn: str jobName: str status: BedrockBatchJobStatus @@ -639,9 +642,10 @@ class BedrockCreateBatchResponse(TypedDict): class BedrockGetBatchResponse(TypedDict, total=False): """ Response structure from getting a Bedrock batch inference job. - + Reference: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetModelInvocationJob.html """ + jobArn: str jobName: str modelId: str diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index 79e3c73dbc..4adb751d90 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -723,6 +723,7 @@ ValidUserMessageContentTypes = [ "input_audio", "audio_url", "document", + "guarded_text", "video_url", "file", ] # used for validating user messages. Prevent users from accidentally sending anthropic messages. diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 9ed66003e7..1b633590a9 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -123,6 +123,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): max_output_tokens: Required[Optional[int]] input_cost_per_token: Required[float] cache_creation_input_token_cost: Optional[float] + cache_creation_input_token_cost_above_1hr: Optional[float] cache_read_input_token_cost: Optional[float] input_cost_per_character: Optional[float] # only for vertex ai models input_cost_per_audio_token: Optional[float] @@ -162,7 +163,9 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): SearchContextCostPerQuery ] # Cost for using web search tool citation_cost_per_token: Optional[float] # Cost per citation token for Perplexity - tiered_pricing: Optional[List[Dict[str, Any]]] # Tiered pricing structure for models like Dashscope + tiered_pricing: Optional[ + List[Dict[str, Any]] + ] # Tiered pricing structure for models like Dashscope litellm_provider: Required[str] mode: Required[ Literal[ @@ -1807,6 +1810,9 @@ class AdapterCompletionStreamWrapper: class StandardLoggingUserAPIKeyMetadata(TypedDict): user_api_key_hash: Optional[str] # hash of the litellm virtual key used user_api_key_alias: Optional[str] + user_api_key_spend: Optional[float] + user_api_key_max_budget: Optional[float] + user_api_key_budget_reset_at: Optional[str] user_api_key_org_id: Optional[str] user_api_key_team_id: Optional[str] user_api_key_user_id: Optional[str] diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 96e19d022c..5e5bccb81e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -1,2570 +1,821 @@ { - "sample_spec": { - "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.", - "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens", - "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "output_cost_per_reasoning_token": 0.0, - "input_cost_per_audio_token": 0.0, - "litellm_provider": "one of https://docs.litellm.ai/docs/providers", - "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_reasoning": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.0, - "search_context_size_medium": 0.0, - "search_context_size_high": 0.0 - }, - "file_search_cost_per_1k_calls": 0.0, - "file_search_cost_per_gb_per_day": 0.0, - "vector_store_cost_per_gb_per_day": 0.0, - "computer_use_input_cost_per_1k_tokens": 0.0, - "computer_use_output_cost_per_1k_tokens": 0.0, - "code_interpreter_cost_per_session": 0.0, - "supported_regions": [ - "global", - "us-west-2", - "eu-west-1", - "ap-southeast-1", - "ap-northeast-1" - ], - "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD" - }, - "omni-moderation-latest": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-latest-intents": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-2024-09-26": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "gpt-4": { - "max_tokens": 4096, - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "input_cost_per_token": 3e-05, - "output_cost_per_token": 6e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4.1": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-mini": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 1.6e-06, - "input_cost_per_token_batches": 2e-07, - "output_cost_per_token_batches": 8e-07, - "cache_read_input_token_cost": 1e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-mini-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 1.6e-06, - "input_cost_per_token_batches": 2e-07, - "output_cost_per_token_batches": 8e-07, - "cache_read_input_token_cost": 1e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-nano": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 4e-07, - "input_cost_per_token_batches": 5e-08, - "output_cost_per_token_batches": 2e-07, - "cache_read_input_token_cost": 2.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-nano-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 4e-07, - "input_cost_per_token_batches": 5e-08, - "output_cost_per_token_batches": 2e-07, - "cache_read_input_token_cost": 2.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4o": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "watsonx/ibm/granite-3-8b-instruct": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 1024, - "input_cost_per_token": 0.0002, - "output_cost_per_token": 0.0002, - "litellm_provider": "watsonx", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_parallel_function_calling": false, - "supports_vision": false, - "supports_audio_input": false, - "supports_audio_output": false, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true - }, - "watsonx/mistralai/mistral-large": { - "max_tokens": 131072, - "max_input_tokens": 131072, - "max_output_tokens": 16384, - "input_cost_per_token": 3e-06, - "output_cost_per_token": 1e-05, - "litellm_provider": "watsonx", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_parallel_function_calling": false, - "supports_vision": false, - "supports_audio_input": false, - "supports_audio_output": false, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true - }, - "gpt-4o-search-preview-2025-03-11": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-search-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "output_cost_per_token": 1e-05, - "input_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_batches": 5e-06, - "cache_read_input_token_cost": 1.25e-06, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.03, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.05 - } - }, - "gpt-4.5-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 7.5e-05, - "output_cost_per_token": 0.00015, - "input_cost_per_token_batches": 3.75e-05, - "output_cost_per_token_batches": 7.5e-05, - "cache_read_input_token_cost": 3.75e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4.5-preview-2025-02-27": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 7.5e-05, - "output_cost_per_token": 0.00015, - "input_cost_per_token_batches": 3.75e-05, - "output_cost_per_token_batches": 7.5e-05, - "cache_read_input_token_cost": 3.75e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "deprecation_date": "2025-07-14" - }, - "gpt-4o-audio-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 0.0001, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 0.0002, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-audio-preview-2024-12-17": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 4e-05, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 8e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-audio-preview-2024-10-01": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 0.0001, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 0.0002, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-audio-preview-2025-06-03": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 2.5e-06, - "input_cost_per_audio_token": 4e-05, - "output_cost_per_token": 1e-05, - "output_cost_per_audio_token": 8e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-mini-audio-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 1.5e-07, - "input_cost_per_audio_token": 1e-05, - "output_cost_per_token": 6e-07, - "output_cost_per_audio_token": 2e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-mini-audio-preview-2024-12-17": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 1.5e-07, - "input_cost_per_audio_token": 1e-05, - "output_cost_per_token": 6e-07, - "output_cost_per_audio_token": 2e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-mini": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 6e-07, - "input_cost_per_token_batches": 7.5e-08, - "output_cost_per_token_batches": 3e-07, - "cache_read_input_token_cost": 7.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-mini-search-preview-2025-03-11": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 6e-07, - "input_cost_per_token_batches": 7.5e-08, - "output_cost_per_token_batches": 3e-07, - "cache_read_input_token_cost": 7.5e-08, - "litellm_provider": "openai", - "mode": "chat", - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4o-mini-search-preview": { - "max_tokens": 16384, - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 6e-07, - "input_cost_per_token_batches": 7.5e-08, - "output_cost_per_token_batches": 3e-07, - 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+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "output_vector_size": 1024, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-claude-3-7-sonnet": { + "input_cost_per_token": 2.5e-06, + "input_dbu_cost_per_token": 3.571e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Claude 3.7 conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.7857e-05, + "output_db_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-gte-large-en": { + "input_cost_per_token": 1.2999e-07, + "input_dbu_cost_per_token": 1.857e-06, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "output_vector_size": 1024, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-llama-2-70b-chat": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "output_dbu_cost_per_token": 2.1429e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-llama-4-maverick": { + "input_cost_per_token": 5e-06, + "input_dbu_cost_per_token": 7.143e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)." + }, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_dbu_cost_per_token": 0.00021429, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-1-405b-instruct": { + "input_cost_per_token": 5e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.500002e-05, + "output_db_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-3-70b-instruct": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.99999e-06, + "output_dbu_cost_per_token": 4.2857e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-70b-instruct": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.99999e-06, + "output_dbu_cost_per_token": 4.2857e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mixtral-8x7b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.9902e-07, + "output_dbu_cost_per_token": 1.4286e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mpt-30b-instruct": { + "input_cost_per_token": 9.9902e-07, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.9902e-07, + "output_dbu_cost_per_token": 1.4286e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mpt-7b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "davinci-002": { + "input_cost_per_token": 2e-06, + "litellm_provider": "text-completion-openai", + "max_input_tokens": 16384, + "max_output_tokens": 4096, + "max_tokens": 16384, + "mode": "completion", + "output_cost_per_token": 2e-06 + }, + "deepgram/base": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-conversationalai": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-finance": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-general": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": 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"input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-voicemail": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-finance": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-general": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + 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"original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-atc": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + 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Defaulting to base model pricing", + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4o-2024-08-06": { + "input_cost_per_token": 3.75e-06, + "input_cost_per_token_batches": 1.875e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_batches": 7.5e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-2024-11-20": { + "cache_creation_input_token_cost": 1.875e-06, + "input_cost_per_token": 3.75e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-mini-2024-07-18": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "output_cost_per_token_batches": 6e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.0-pro": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 32760, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, "gemini-1.0-pro-001": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", + "max_tokens": 8192, "mode": "chat", - "supports_function_calling": true, + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.0-ultra": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", "supports_function_calling": true, - "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.0-ultra-001": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-002": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, + "max_tokens": 8192, + "mode": "chat", "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 1.25e-06, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 2.5e-06, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 5e-06, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 1e-05, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_vision": true, - "supports_pdf_input": true, - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-002": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 1.25e-06, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 2.5e-06, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 5e-06, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 1e-05, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_vision": true, - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-1.5-pro", - "deprecation_date": "2025-09-24", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-001": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 1.25e-06, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 2.5e-06, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 5e-06, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 1e-05, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_vision": true, - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-05-24", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-preview-0514": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 7.8125e-08, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 1.5625e-07, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 3.125e-07, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 6.25e-07, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-preview-0215": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 7.8125e-08, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 1.5625e-07, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 3.125e-07, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 6.25e-07, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-preview-0409": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 7.8125e-08, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 1.5625e-07, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 3.125e-07, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 6.25e-07, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 3e-07, - "output_cost_per_character": 7.5e-08, - "output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-exp-0827": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 4.688e-09, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 4.6875e-09, - "output_cost_per_character": 1.875e-08, - "output_cost_per_token_above_128k_tokens": 9.375e-09, - "output_cost_per_character_above_128k_tokens": 3.75e-08, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-002": { - "max_tokens": 8192, - "max_input_tokens": 1048576, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 3e-07, - "output_cost_per_character": 7.5e-08, - "output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-1.5-flash", - "deprecation_date": "2025-09-24", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-001": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 3e-07, - "output_cost_per_character": 7.5e-08, - "output_cost_per_token_above_128k_tokens": 6e-07, - "output_cost_per_character_above_128k_tokens": 1.5e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-05-24", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-preview-0514": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_image": 2e-05, - "input_cost_per_video_per_second": 2e-05, - "input_cost_per_audio_per_second": 2e-06, - "input_cost_per_token": 7.5e-08, - "input_cost_per_character": 1.875e-08, - "input_cost_per_token_above_128k_tokens": 1e-06, - "input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 4.6875e-09, - "output_cost_per_character": 1.875e-08, - "output_cost_per_token_above_128k_tokens": 9.375e-09, - "output_cost_per_character_above_128k_tokens": 3.75e-08, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-pro-experimental": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_token": 0, - "output_cost_per_token": 0, - "input_cost_per_character": 0, - "output_cost_per_character": 0, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": false, - "supports_tool_choice": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental", - "supports_parallel_function_calling": true - }, - "gemini-flash-experimental": { - "max_tokens": 8192, - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "input_cost_per_token": 0, - "output_cost_per_token": 0, - "input_cost_per_character": 0, - "output_cost_per_character": 0, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": false, - "supports_tool_choice": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental", - "supports_parallel_function_calling": true - }, - "gemini-pro-vision": { - "max_tokens": 2048, - "max_input_tokens": 16384, - "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, - "max_video_length": 2, - "input_cost_per_token": 5e-07, "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-vision": { - "max_tokens": 2048, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, - "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", + "max_videos_per_prompt": 1, "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, "gemini-1.0-pro-vision-001": { - "max_tokens": 2048, + "deprecation_date": "2025-04-09", + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, + "max_videos_per_prompt": 1, + "mode": "chat", "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", "supports_function_calling": true, - "supports_vision": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, - "medlm-medium": { - "max_tokens": 8192, - "max_input_tokens": 32768, - "max_output_tokens": 8192, - "input_cost_per_character": 5e-07, - "output_cost_per_character": 1e-06, + "gemini-1.0-ultra": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true - }, - "medlm-large": { - "max_tokens": 1024, "max_input_tokens": 8192, - "max_output_tokens": 1024, - "input_cost_per_character": 5e-06, - "output_cost_per_character": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", + "max_output_tokens": 2048, + "max_tokens": 8192, "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "gemini-2.5-pro-exp-03-25": { - "max_tokens": 65535, - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, + "gemini-1.0-ultra-001": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 8192, + "max_output_tokens": 2048, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true + }, + "gemini-1.5-flash": { + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", "max_audio_length_hours": 8.4, "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, "max_pdf_size_mb": 30, - "input_cost_per_token": 1.25e-06, - "input_cost_per_token_above_200k_tokens": 2.5e-06, - "output_cost_per_token": 1e-05, - "output_cost_per_token_above_200k_tokens": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_video_input": true, - "supports_pdf_input": true, - "supports_response_schema": true, - "supports_tool_choice": true, - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" - ], - "supported_modalities": [ - "text", - "image", - "audio", - "video" - ], - "supported_output_modalities": [ - "text" - ], - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supports_parallel_function_calling": true, - "supports_web_search": true, - "cache_read_input_token_cost": 3.125e-07, - "supports_prompt_caching": true - }, - "gemini-2.0-pro-exp-02-05": { "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_character": 7.5e-08, + "output_cost_per_character_above_128k_tokens": 1.5e-07, + "output_cost_per_token": 3e-07, + "output_cost_per_token_above_128k_tokens": 6e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.5-flash-001": { + "deprecation_date": "2025-05-24", + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_pdf_size_mb": 30, + "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_character": 7.5e-08, + "output_cost_per_character_above_128k_tokens": 1.5e-07, + "output_cost_per_token": 3e-07, + "output_cost_per_token_above_128k_tokens": 6e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.5-flash-002": { + "deprecation_date": "2025-09-24", + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 8192, + "max_pdf_size_mb": 30, + "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_character": 7.5e-08, + "output_cost_per_character_above_128k_tokens": 1.5e-07, + "output_cost_per_token": 3e-07, + "output_cost_per_token_above_128k_tokens": 6e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-1.5-flash", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.5-flash-exp-0827": { + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 4.688e-09, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_pdf_size_mb": 30, + "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_character": 1.875e-08, + "output_cost_per_character_above_128k_tokens": 3.75e-08, + "output_cost_per_token": 4.6875e-09, + "output_cost_per_token_above_128k_tokens": 9.375e-09, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.5-flash-preview-0514": { + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_pdf_size_mb": 30, + "max_tokens": 8192, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_character": 1.875e-08, + "output_cost_per_character_above_128k_tokens": 3.75e-08, + "output_cost_per_token": 4.6875e-09, + "output_cost_per_token_above_128k_tokens": 9.375e-09, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.5-pro": { + "input_cost_per_audio_per_second": 3.125e-05, + "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, + "input_cost_per_character": 3.125e-07, + "input_cost_per_character_above_128k_tokens": 6.25e-07, + "input_cost_per_image": 0.00032875, + "input_cost_per_image_above_128k_tokens": 0.0006575, + "input_cost_per_token": 1.25e-06, + "input_cost_per_token_above_128k_tokens": 2.5e-06, + "input_cost_per_video_per_second": 0.00032875, + "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 2097152, "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_pdf_size_mb": 30, - "input_cost_per_token": 1.25e-06, - "input_cost_per_token_above_200k_tokens": 2.5e-06, - "output_cost_per_token": 1e-05, - "output_cost_per_token_above_200k_tokens": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_video_input": true, - "supports_pdf_input": true, - "supports_response_schema": true, - "supports_tool_choice": true, - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" - ], - "supported_modalities": [ - "text", - "image", - "audio", - "video" - ], - "supported_output_modalities": [ - "text" - ], - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supports_parallel_function_calling": true, - "supports_web_search": true, - "cache_read_input_token_cost": 3.125e-07, - "supports_prompt_caching": true - }, - "gemini-2.0-flash-exp": { "max_tokens": 8192, - "max_input_tokens": 1048576, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - "max_audio_length_hours": 8.4, - 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"sambanova/Meta-Llama-3.3-70B-Instruct": { + "input_cost_per_token": 6e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-Guard-3-8B": { + "input_cost_per_token": 3e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 3e-07, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/QwQ-32B": { + "input_cost_per_token": 5e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1e-06, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/Qwen2-Audio-7B-Instruct": { + "input_cost_per_token": 5e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 0.0001, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_audio_input": true + }, + "sambanova/Qwen3-32B": { + "input_cost_per_token": 4e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 8e-07, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "sample_spec": { + "code_interpreter_cost_per_session": 0.0, + "computer_use_input_cost_per_1k_tokens": 0.0, + "computer_use_output_cost_per_1k_tokens": 0.0, + "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD", + 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"litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "xai/grok-vision-beta": { + "input_cost_per_image": 5e-06, + "input_cost_per_token": 5e-06, + "litellm_provider": "xai", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true } -} +} \ No newline at end of file diff --git a/tests/batches_tests/test_bedrock_files_and_batches.py b/tests/batches_tests/test_bedrock_files_and_batches.py index 3f90411f62..d082ed41ea 100644 --- a/tests/batches_tests/test_bedrock_files_and_batches.py +++ b/tests/batches_tests/test_bedrock_files_and_batches.py @@ -75,6 +75,19 @@ async def test_async_file_and_batch(): ) print("CREATED BATCH RESPONSE=", create_batch_response) + # retrieve batch + retrieve_batch_response = await litellm.aretrieve_batch( + batch_id=create_batch_response.id, + custom_llm_provider="bedrock", + model="us.anthropic.claude-3-5-sonnet-20240620-v1:0", + ) + print("RETRIEVED BATCH RESPONSE=", retrieve_batch_response) + + # Validate the response + assert retrieve_batch_response.id == create_batch_response.id + assert retrieve_batch_response.object == "batch" + assert retrieve_batch_response.status in ["validating", "in_progress", "completed", "failed", "cancelled"] + @pytest.mark.asyncio() async def test_mock_bedrock_file_url_mapping(): @@ -118,3 +131,65 @@ async def test_mock_bedrock_file_url_mapping(): expected_s3_uri, _ = bedrock_config._convert_https_url_to_s3_uri(captured_put_url) assert file_obj.id == expected_s3_uri + +@pytest.mark.asyncio() +async def test_bedrock_retrieve_batch(): + """ + Test bedrock batch retrieval functionality, validating that input and output file IDs + are correctly extracted from the Bedrock response and included in the final transformed response. + """ + print("Testing bedrock batch retrieval") + + # Mock bedrock batch response + mock_bedrock_response = { + "jobArn": "arn:aws:bedrock:us-west-2:123456789012:model-invocation-job/test-job-123", + "jobName": "test-job-123", + "modelId": "us.anthropic.claude-3-5-sonnet-20240620-v1:0", + "roleArn": "arn:aws:iam::123456789012:role/service-role/AmazonBedrockExecutionRoleForAgents_TEST", + "status": "InProgress", + "message": "Job is in progress", + "submitTime": "2024-01-01T12:00:00Z", + "lastModifiedTime": "2024-01-01T12:30:00Z", + "inputDataConfig": { + "s3InputDataConfig": { + "s3Uri": "s3://test-bucket/input/test-input.jsonl" + } + }, + "outputDataConfig": { + "s3OutputDataConfig": { + "s3Uri": "s3://test-bucket/output/" + } + } + } + + # Mock the HTTP response + mock_response = MagicMock() + mock_response.json.return_value = mock_bedrock_response + mock_response.status_code = 200 + + # Print the mock response to debug + print("MOCK RESPONSE DATA:", mock_bedrock_response) + + with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.get") as mock_get: + mock_response.raise_for_status.return_value = None + mock_get.return_value = mock_response + + # Test retrieve batch + batch_response = await litellm.aretrieve_batch( + batch_id="arn:aws:bedrock:us-west-2:123456789012:model-invocation-job/test-job-123", + custom_llm_provider="bedrock", + model="us.anthropic.claude-3-5-sonnet-20240620-v1:0", + ) + + print("MOCKED BATCH RESPONSE=", batch_response) + + # Validate the response + assert batch_response.id == "arn:aws:bedrock:us-west-2:123456789012:model-invocation-job/test-job-123" + assert batch_response.object == "batch" + assert batch_response.status == "in_progress" # Bedrock "InProgress" maps to "in_progress" + assert batch_response.endpoint == "/v1/chat/completions" + + # Validate input and output file IDs in the final transformed response + assert batch_response.input_file_id == "s3://test-bucket/input/test-input.jsonl" + assert batch_response.output_file_id == "s3://test-bucket/output/" + diff --git a/tests/llm_translation/test_cohere_generate_api.py b/tests/llm_translation/test_cohere_generate_api.py deleted file mode 100644 index 837e077cde..0000000000 --- a/tests/llm_translation/test_cohere_generate_api.py +++ /dev/null @@ -1,203 +0,0 @@ -import os -import sys -import traceback - -from dotenv import load_dotenv - -load_dotenv() -import io -import os - -sys.path.insert( - 0, os.path.abspath("../..") -) # Adds the parent directory to the system path -import json - -import pytest - -import litellm -from litellm import completion -from litellm.llms.cohere.completion.transformation import CohereTextConfig - - -def test_cohere_generate_api_completion(): - try: - from litellm.llms.custom_httpx.http_handler import HTTPHandler - from unittest.mock import patch, MagicMock - - client = HTTPHandler() - litellm.set_verbose = True - messages = [ - {"role": "system", "content": "You're a good bot"}, - { - "role": "user", - "content": "Hey", - }, - ] - - with patch.object(client, "post") as mock_client: - try: - completion( - model="cohere/command", - messages=messages, - max_tokens=10, - client=client, - ) - except Exception as e: - print(e) - mock_client.assert_called_once() - print("mock_client.call_args.kwargs", mock_client.call_args.kwargs) - - assert ( - mock_client.call_args.kwargs["url"] - == "https://api.cohere.ai/v1/generate" - ) - json_data = json.loads(mock_client.call_args.kwargs["data"]) - assert json_data["model"] == "command" - assert json_data["prompt"] == "You're a good bot Hey" - assert json_data["max_tokens"] == 10 - except Exception as e: - pytest.fail(f"Error occurred: {e}") - - -@pytest.mark.asyncio -async def test_cohere_generate_api_stream(): - try: - litellm.set_verbose = True - messages = [ - {"role": "system", "content": "You're a good bot"}, - { - "role": "user", - "content": "Hey", - }, - ] - response = await litellm.acompletion( - model="cohere/command", - messages=messages, - max_tokens=10, - stream=True, - ) - print("async cohere stream response", response) - async for chunk in response: - print(chunk) - except Exception as e: - pytest.fail(f"Error occurred: {e}") - - -def test_completion_cohere_stream_bad_key(): - try: - api_key = "bad-key" - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - { - "role": "user", - "content": "how does a court case get to the Supreme Court?", - }, - ] - completion( - model="command", - messages=messages, - stream=True, - max_tokens=50, - api_key=api_key, - ) - - except litellm.AuthenticationError as e: - pass - except Exception as e: - pytest.fail(f"Error occurred: {e}") - - -def test_cohere_transform_request(): - try: - config = CohereTextConfig() - messages = [ - {"role": "system", "content": "You're a helpful bot"}, - {"role": "user", "content": "Hello"}, - ] - optional_params = {"max_tokens": 10, "temperature": 0.7} - headers = {} - - transformed_request = config.transform_request( - model="command", - messages=messages, - optional_params=optional_params, - litellm_params={}, - headers=headers, - ) - - print("transformed_request", json.dumps(transformed_request, indent=4)) - - assert transformed_request["model"] == "command" - assert transformed_request["prompt"] == "You're a helpful bot Hello" - assert transformed_request["max_tokens"] == 10 - assert transformed_request["temperature"] == 0.7 - except Exception as e: - pytest.fail(f"Error occurred: {e}") - - -def test_cohere_transform_request_with_tools(): - try: - config = CohereTextConfig() - messages = [{"role": "user", "content": "What's the weather?"}] - tools = [ - { - "type": "function", - "function": { - "name": "get_weather", - "description": "Get weather information", - "parameters": { - "type": "object", - "properties": {"location": {"type": "string"}}, - }, - }, - } - ] - optional_params = {"tools": tools} - - transformed_request = config.transform_request( - model="command", - messages=messages, - optional_params=optional_params, - litellm_params={}, - headers={}, - ) - - print("transformed_request", json.dumps(transformed_request, indent=4)) - assert "tools" in transformed_request - assert transformed_request["tools"] == {"tools": tools} - except Exception as e: - pytest.fail(f"Error occurred: {e}") - - -def test_cohere_map_openai_params(): - try: - config = CohereTextConfig() - openai_params = { - "temperature": 0.7, - "max_tokens": 100, - "n": 2, - "top_p": 0.9, - "frequency_penalty": 0.5, - "presence_penalty": 0.5, - "stop": ["END"], - "stream": True, - } - - mapped_params = config.map_openai_params( - non_default_params=openai_params, - optional_params={}, - model="command", - drop_params=False, - ) - - assert mapped_params["temperature"] == 0.7 - assert mapped_params["max_tokens"] == 100 - assert mapped_params["num_generations"] == 2 - assert mapped_params["p"] == 0.9 - assert mapped_params["frequency_penalty"] == 0.5 - assert mapped_params["presence_penalty"] == 0.5 - assert mapped_params["stop_sequences"] == ["END"] - assert mapped_params["stream"] == True - except Exception as e: - pytest.fail(f"Error occurred: {e}") diff --git a/tests/local_testing/test_anthropic_prompt_caching.py b/tests/local_testing/test_anthropic_prompt_caching.py index 4c2b66879e..f2c194a36e 100644 --- a/tests/local_testing/test_anthropic_prompt_caching.py +++ b/tests/local_testing/test_anthropic_prompt_caching.py @@ -300,6 +300,76 @@ async def test_anthropic_api_prompt_caching_basic(): ) +@pytest.mark.asyncio() +async def test_anthropic_api_prompt_caching_basic_with_cache_creation(): + from uuid import uuid4 + + random_id = uuid4() + + litellm.set_verbose = True + response = await litellm.acompletion( + model="anthropic/claude-3-5-sonnet-20240620", + messages=[ + # System Message + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Here is the full text of a complex legal agreement {}".format( + random_id + ) + * 400, + "cache_control": {"type": "ephemeral"}, + } + ], + }, + # marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + { + "role": "assistant", + "content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo", + }, + # The final turn is marked with cache-control, for continuing in followups. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + ], + temperature=0.2, + max_tokens=10, + extra_headers={ + "anthropic-version": "2023-06-01", + "anthropic-beta": "prompt-caching-2024-07-31", + }, + ) + + print("response=", response) + + assert "cache_read_input_tokens" in response.usage + assert "cache_creation_input_tokens" in response.usage + + # Assert either a cache entry was created or cache was read - changes depending on the anthropic api ttl + assert (response.usage.cache_read_input_tokens > 0) or ( + response.usage.cache_creation_input_tokens > 0 + ) + + @pytest.mark.asyncio() async def test_anthropic_api_prompt_caching_with_content_str(): system_message = [ diff --git a/tests/openai_endpoints_tests/test_e2e_openai_responses_api.py b/tests/openai_endpoints_tests/test_e2e_openai_responses_api.py index de60881820..d5208421f1 100644 --- a/tests/openai_endpoints_tests/test_e2e_openai_responses_api.py +++ b/tests/openai_endpoints_tests/test_e2e_openai_responses_api.py @@ -145,9 +145,6 @@ def test_cancel_response(): # verify cancel response structure assert hasattr(cancel_response, "id") - # Note: Cancel response returns ResponsesAPIResponse, not DeleteResponseResult - # The actual response structure depends on the provider implementation - assert isinstance(cancel_response, ResponsesAPIResponse) except Exception as e: if "Cannot cancel a completed response" in str(e): pass @@ -179,9 +176,6 @@ def test_cancel_streaming_response(): cancel_response = client.responses.cancel(response_id) print("CANCEL streaming response=", cancel_response) assert hasattr(cancel_response, "id") - # Note: Cancel response returns ResponsesAPIResponse, not DeleteResponseResult - # The actual response structure depends on the provider implementation - assert isinstance(cancel_response, ResponsesAPIResponse) except Exception as e: if "Cannot cancel a completed response" in str(e): pass diff --git a/tests/proxy_unit_tests/test_auth_checks.py b/tests/proxy_unit_tests/test_auth_checks.py index 2c829406c0..24c005443a 100644 --- a/tests/proxy_unit_tests/test_auth_checks.py +++ b/tests/proxy_unit_tests/test_auth_checks.py @@ -48,7 +48,7 @@ async def test_get_end_user_object(customer_spend, customer_budget): ) _cache = DualCache() _key = "end_user_id:{}".format(end_user_id) - _cache.set_cache(key=_key, value=end_user_obj) + _cache.set_cache(key=_key, value=end_user_obj.model_dump()) try: await get_end_user_object( end_user_id=end_user_id, diff --git a/tests/proxy_unit_tests/test_google_gemini_proxy_request.py b/tests/proxy_unit_tests/test_google_gemini_proxy_request.py index a8c6741940..388dd4ab17 100644 --- a/tests/proxy_unit_tests/test_google_gemini_proxy_request.py +++ b/tests/proxy_unit_tests/test_google_gemini_proxy_request.py @@ -345,6 +345,130 @@ async def test_generationconfig_to_config_mapping(sample_request_payload): print("✅ generationConfig to config mapping test passed") +@pytest.mark.asyncio +async def test_gemini_custom_api_base_proxy_integration(): + """ + Test that Gemini models work correctly with custom API base URLs in proxy context. + + This test verifies that when a custom api_base is provided for Gemini models, + the URL is correctly constructed using the _check_custom_proxy method. + """ + from litellm.llms.vertex_ai.vertex_llm_base import VertexBase + + # Test the _check_custom_proxy method directly + vertex_base = VertexBase() + + # Test case 1: Custom API base for Gemini + custom_api_base = "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta" + model = "gemini-2.5-flash-lite" + endpoint = "generateContent" + + auth_header, result_url = vertex_base._check_custom_proxy( + api_base=custom_api_base, + custom_llm_provider="gemini", + gemini_api_key="test-api-key", + endpoint=endpoint, + stream=False, + auth_header=None, + url=f"https://generativelanguage.googleapis.com/v1beta/models/{model}:{endpoint}", + model=model, + ) + + # Verify the URL is correctly constructed + expected_url = f"{custom_api_base}/models/{model}:{endpoint}" + assert result_url == expected_url, f"Expected {expected_url}, got {result_url}" + + # Verify the auth header is set to the API key + assert auth_header == "test-api-key", f"Expected 'test-api-key', got {auth_header}" + + print(f"✅ Custom API base URL construction test passed: {result_url}") + + # Test case 2: Custom API base with streaming + auth_header_streaming, result_url_streaming = vertex_base._check_custom_proxy( + api_base=custom_api_base, + custom_llm_provider="gemini", + gemini_api_key="test-api-key", + endpoint=endpoint, + stream=True, + auth_header=None, + url=f"https://generativelanguage.googleapis.com/v1beta/models/{model}:{endpoint}", + model=model, + ) + + # Verify streaming URL has ?alt=sse parameter + expected_streaming_url = f"{custom_api_base}/models/{model}:{endpoint}?alt=sse" + assert result_url_streaming == expected_streaming_url, f"Expected {expected_streaming_url}, got {result_url_streaming}" + + print(f"✅ Custom API base streaming URL test passed: {result_url_streaming}") + + # Test case 3: Error handling - missing API key + with pytest.raises(ValueError, match="Missing gemini_api_key"): + vertex_base._check_custom_proxy( + api_base=custom_api_base, + custom_llm_provider="gemini", + gemini_api_key=None, # Missing API key + endpoint=endpoint, + stream=False, + auth_header=None, + url=f"https://generativelanguage.googleapis.com/v1beta/models/{model}:{endpoint}", + model=model, + ) + + print("✅ Missing API key error handling test passed") + + +@pytest.mark.asyncio +async def test_gemini_proxy_config_with_custom_api_base(): + """ + Test that proxy configuration correctly handles custom API base for Gemini models. + + This test simulates the proxy configuration scenario where a model is configured + with a custom api_base in the config.yaml file. + """ + from litellm.llms.vertex_ai.vertex_llm_base import VertexBase + + # Simulate proxy configuration + model_config = { + "model_name": "byok-gemini/*", + "litellm_params": { + "model": "gemini/*", + "api_key": "dummy-key-for-testing", + "api_base": "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta" + } + } + + vertex_base = VertexBase() + + # Test with different Gemini models + test_models = [ + "gemini-2.5-flash-lite", + "gemini-2.5-pro", + "gemini-1.5-flash", + "gemini-1.5-pro" + ] + + for model in test_models: + # Test generateContent endpoint + auth_header, result_url = vertex_base._check_custom_proxy( + api_base=model_config["litellm_params"]["api_base"], + custom_llm_provider="gemini", + gemini_api_key=model_config["litellm_params"]["api_key"], + endpoint="generateContent", + stream=False, + auth_header=None, + url=f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent", + model=model, + ) + + expected_url = f"{model_config['litellm_params']['api_base']}/models/{model}:generateContent" + assert result_url == expected_url, f"Expected {expected_url}, got {result_url} for model {model}" + assert auth_header == model_config["litellm_params"]["api_key"], f"Expected API key, got {auth_header} for model {model}" + + print(f"✅ Model {model} configuration test passed: {result_url}") + + print("✅ Proxy configuration with custom API base test passed") + + if __name__ == "__main__": # Run the tests pytest.main([__file__, "-v"]) diff --git a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py b/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py index 39b8427fcf..e715fec4ff 100644 --- a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py +++ b/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py @@ -1,7 +1,7 @@ import asyncio import os import sys -from datetime import datetime, timedelta +from datetime import datetime, timedelta, timezone from typing import Optional from unittest.mock import Mock, patch, MagicMock @@ -661,17 +661,24 @@ def create_standard_logging_payload_with_tool_calls() -> StandardLoggingPayload: """Create a StandardLoggingPayload object with tool calls for testing""" return { "id": "test-request-id-tool-calls", + "trace_id": "test-trace-id-tool-calls", "call_type": "completion", + "stream": None, "response_cost": 0.05, "response_cost_failure_debug_info": None, "status": "success", + "custom_llm_provider": "openai", "total_tokens": 50, "prompt_tokens": 20, "completion_tokens": 30, "startTime": 1234567890.0, "endTime": 1234567891.0, "completionStartTime": 1234567890.5, - "model_map_information": {"model_map_key": "gpt-4", "model_map_value": None}, + "response_time": 1.0, + "model_map_information": { + "model_map_key": "gpt-4", + "model_map_value": None + }, "model": "gpt-4", "model_id": "model-123", "model_group": "openai-gpt", @@ -746,6 +753,7 @@ def create_standard_logging_payload_with_tool_calls() -> StandardLoggingPayload: ] }, "error_str": None, + "error_information": None, "model_parameters": {"temperature": 0.7}, "hidden_params": { "model_id": "model-123", @@ -758,14 +766,9 @@ def create_standard_logging_payload_with_tool_calls() -> StandardLoggingPayload: "litellm_model_name": None, "usage_object": None, }, - "stream": None, - "response_time": 1.0, - "error_information": None, "guardrail_information": None, "standard_built_in_tools_params": None, - "trace_id": "test-trace-id-tool-calls", - "custom_llm_provider": "openai", - } + } # type: ignore class TestDataDogLLMObsLoggerToolCalls: @@ -897,3 +900,204 @@ class TestDataDogLLMObsLoggerToolCalls: assert len(output_tool_calls) == 1 output_function_info = output_tool_calls[0].get("function", {}) assert output_function_info.get("name") == "format_response" + +def create_standard_logging_payload_with_spend_metrics() -> StandardLoggingPayload: + """Create a StandardLoggingPayload object with spend metrics for testing""" + from datetime import datetime, timezone + + # Create a budget reset time 10 days from now (using "10d" format) + budget_reset_at = datetime.now(timezone.utc) + timedelta(days=10) + + return { + "id": "test-request-id-spend", + "trace_id": "test-trace-id-spend", + "call_type": "completion", + "stream": None, + "response_cost": 0.15, + "response_cost_failure_debug_info": None, + "status": "success", + "custom_llm_provider": "openai", + "total_tokens": 30, + "prompt_tokens": 10, + "completion_tokens": 20, + "startTime": 1234567890.0, + "endTime": 1234567891.0, + "completionStartTime": 1234567890.5, + "response_time": 1.0, + "model_map_information": { + "model_map_key": "gpt-4", + "model_map_value": None + }, + "model": "gpt-4", + "model_id": "model-123", + "model_group": "openai-gpt", + "api_base": "https://api.openai.com", + "metadata": { + "user_api_key_hash": "test_hash", + "user_api_key_org_id": None, + "user_api_key_alias": "test_alias", + "user_api_key_team_id": "test_team", + "user_api_key_user_id": "test_user", + "user_api_key_team_alias": "test_team_alias", + "user_api_key_user_email": None, + "user_api_key_end_user_id": None, + "user_api_key_request_route": None, + "user_api_key_spend": 0.67, + "user_api_key_max_budget": 10.0, # $10 max budget + "user_api_key_budget_reset_at": budget_reset_at.isoformat(), # ISO format: 2025-09-26T... + "spend_logs_metadata": None, + "requester_ip_address": "127.0.0.1", + "requester_metadata": None, + "requester_custom_headers": None, + "prompt_management_metadata": None, + "mcp_tool_call_metadata": None, + "vector_store_request_metadata": None, + "applied_guardrails": None, + "usage_object": None, + "cold_storage_object_key": None, + }, + "cache_hit": False, + "cache_key": None, + "saved_cache_cost": 0.0, + "request_tags": [], + "end_user": None, + "requester_ip_address": "127.0.0.1", + "messages": [{"role": "user", "content": "Hello, world!"}], + "response": {"choices": [{"message": {"content": "Hi there!"}}]}, + "error_str": None, + "error_information": None, + "model_parameters": {"stream": False}, + "hidden_params": { + "model_id": "model-123", + "cache_key": None, + "api_base": "https://api.openai.com", + "response_cost": "0.15", + "litellm_overhead_time_ms": None, + "additional_headers": None, + "batch_models": None, + "litellm_model_name": None, + "usage_object": None, + }, + "guardrail_information": None, + "standard_built_in_tools_params": None, + } # type: ignore + + +@pytest.mark.asyncio +async def test_datadog_llm_obs_spend_metrics(mock_env_vars): + """Test that budget metrics are properly extracted and logged""" + datadog_llm_obs_logger = DataDogLLMObsLogger() + + # Create a standard logging payload with spend metrics + payload = create_standard_logging_payload_with_spend_metrics() + + # Show the budget reset time in ISO format + budget_reset_iso = payload["metadata"]["user_api_key_budget_reset_at"] + print(f"Budget reset time (ISO format): {budget_reset_iso}") + from datetime import datetime, timezone + print(f"Current time: {datetime.now(timezone.utc).isoformat()}") + + # Test the _get_spend_metrics method + spend_metrics = datadog_llm_obs_logger._get_spend_metrics(payload) + + # Verify budget metrics are present + assert "user_api_key_max_budget" in spend_metrics + assert spend_metrics["user_api_key_max_budget"] == 10.0 + + assert "user_api_key_budget_reset_at" in spend_metrics + # The budget reset should be a datetime string in ISO format + budget_reset = spend_metrics["user_api_key_budget_reset_at"] + assert isinstance(budget_reset, str) + print(f"Budget reset datetime: {budget_reset}") + # Should be close to 10 days from now + budget_reset_dt = datetime.fromisoformat(budget_reset.replace('Z', '+00:00')) + now = datetime.now(timezone.utc) + time_diff = (budget_reset_dt - now).total_seconds() / 86400 # days + assert 9.5 <= time_diff <= 10.5 # Should be close to 10 days + + print(f"Spend metrics: {spend_metrics}") + + +@pytest.mark.asyncio +async def test_datadog_llm_obs_spend_metrics_no_budget(mock_env_vars): + """Test that spend metrics work when no budget is set""" + datadog_llm_obs_logger = DataDogLLMObsLogger() + + # Create a standard logging payload without budget metadata + payload = create_standard_logging_payload_with_spend_metrics() + + # Remove budget-related metadata to test no-budget scenario + payload["metadata"].pop("user_api_key_max_budget", None) + payload["metadata"].pop("user_api_key_budget_reset_at", None) + + # Test the _get_spend_metrics method + spend_metrics = datadog_llm_obs_logger._get_spend_metrics(payload) + + # Verify only response cost is present + assert "response_cost" in spend_metrics + assert spend_metrics["response_cost"] == 0.15 + + # Budget metrics should not be present + assert "user_api_key_max_budget" not in spend_metrics + assert "user_api_key_budget_reset_at" not in spend_metrics + + print(f"Spend metrics (no budget): {spend_metrics}") + + +@pytest.mark.asyncio +async def test_spend_metrics_in_datadog_payload(mock_env_vars): + """Test that spend metrics are correctly included in DataDog LLM Observability payloads""" + from datetime import datetime + datadog_llm_obs_logger = DataDogLLMObsLogger() + + standard_payload = create_standard_logging_payload_with_spend_metrics() + + kwargs = { + "standard_logging_object": standard_payload, + "litellm_params": {"metadata": {}}, + } + + start_time = datetime.now() + end_time = datetime.now() + + payload = datadog_llm_obs_logger.create_llm_obs_payload(kwargs, start_time, end_time) + + # Verify basic payload structure + assert payload.get("name") == "litellm_llm_call" + assert payload.get("status") == "ok" + + # Verify spend metrics are included in metadata + meta = payload.get("meta", {}) + assert meta is not None, "Meta section should exist in payload" + + metadata = meta.get("metadata", {}) + assert metadata is not None, "Metadata section should exist in meta" + + spend_metrics = metadata.get("spend_metrics", {}) + assert spend_metrics, "Spend metrics should exist in metadata" + + # Check that all metrics are present + assert "response_cost" in spend_metrics + assert "user_api_key_spend" in spend_metrics + assert "user_api_key_max_budget" in spend_metrics + assert "user_api_key_budget_reset_at" in spend_metrics + + # Verify the values are correct + assert spend_metrics["response_cost"] == 0.15 # response_cost + assert spend_metrics["user_api_key_spend"] == 0.67 # lol + assert spend_metrics["user_api_key_max_budget"] == 10.0 # max budget + + # Verify budget reset is a datetime string in ISO format + budget_reset = spend_metrics["user_api_key_budget_reset_at"] + assert isinstance(budget_reset, str) + print(f"Budget reset in payload: {budget_reset}") # In StandardLoggingUserAPIKeyMetadata + user_api_key_budget_reset_at: Optional[str] = None + + # In DDLLMObsSpendMetrics + user_api_key_budget_reset_at: str + # Should be close to 10 days from now + from datetime import datetime, timezone + budget_reset_dt = datetime.fromisoformat(budget_reset.replace('Z', '+00:00')) + now = datetime.now(timezone.utc) + time_diff = (budget_reset_dt - now).total_seconds() / 86400 # days + assert 9.5 <= time_diff <= 10.5 # Should be close to 10 days diff --git a/tests/test_litellm/integrations/test_langfuse_otel.py b/tests/test_litellm/integrations/test_langfuse_otel.py index ceae019d34..59b1417832 100644 --- a/tests/test_litellm/integrations/test_langfuse_otel.py +++ b/tests/test_litellm/integrations/test_langfuse_otel.py @@ -6,6 +6,8 @@ import pytest from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger from litellm.types.integrations.langfuse_otel import LangfuseOtelConfig +from litellm.types.llms.openai import ResponsesAPIResponse +from datetime import datetime class TestLangfuseOtelIntegration: @@ -241,6 +243,134 @@ class TestLangfuseOtelIntegration: _ = LangfuseOtelLogger.get_langfuse_otel_config() assert os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT") == "https://otel-host.com/api/public/otel" + + +class TestLangfuseOtelResponsesAPI: + """Test suite for Langfuse OTEL integration with ResponsesAPI""" + + def test_langfuse_otel_with_responses_api(self): + """Test that Langfuse OTEL logger works with ResponsesAPI responses and logs metadata.""" + # Create a mock ResponsesAPIResponse + mock_response = ResponsesAPIResponse( + id="response-123", + created_at=1234567890, + output=[ + { + "type": "message", + "content": [{"type": "text", "text": "Hello from responses API"}] + } + ], + parallel_tool_calls=False, + tool_choice="auto", + tools=[], + top_p=1.0 + ) + + # Create kwargs with metadata that should be logged + test_metadata = { + "user_id": "test123", + "session_id": "abc456", + "custom_field": "test_value", + "generation_name": "responses_test_generation", + "trace_name": "responses_api_trace" + } + + kwargs = { + "call_type": "responses", + "messages": [{"role": "user", "content": "Hello"}], + "model": "gpt-4o", + "optional_params": {}, + "litellm_params": {"metadata": test_metadata} + } + + mock_span = MagicMock() + + with patch('litellm.integrations.arize._utils.set_attributes') as mock_set_attributes: + with patch('litellm.integrations.arize._utils.safe_set_attribute') as mock_safe_set_attribute: + logger = LangfuseOtelLogger() + logger.set_langfuse_otel_attributes(mock_span, kwargs, mock_response) + + # Verify that set_attributes was called for general attributes + mock_set_attributes.assert_called_once_with(mock_span, kwargs, mock_response) + + # Verify that Langfuse-specific attributes were set + mock_safe_set_attribute.assert_any_call( + mock_span, "langfuse.generation.name", "responses_test_generation" + ) + mock_safe_set_attribute.assert_any_call( + mock_span, "langfuse.trace.name", "responses_api_trace" + ) + + def test_responses_api_metadata_extraction(self): + """Test that metadata is correctly extracted from ResponsesAPI kwargs.""" + # Clean up any existing module mocks + import sys + if "litellm.integrations.langfuse.langfuse" in sys.modules: + original_module = sys.modules["litellm.integrations.langfuse.langfuse"] + + test_metadata = { + "user_id": "responses_user_123", + "session_id": "responses_session_456", + "custom_metadata": {"key": "value"}, + "generation_name": "responses_generation", + "trace_id": "custom_trace_id" + } + + kwargs = { + "call_type": "responses", + "model": "gpt-4o", + "litellm_params": {"metadata": test_metadata} + } + + extracted_metadata = LangfuseOtelLogger._extract_langfuse_metadata(kwargs) + + # Verify all expected metadata was extracted (may have additional fields from header enrichment) + for key, value in test_metadata.items(): + assert extracted_metadata[key] == value + + assert extracted_metadata["user_id"] == "responses_user_123" + assert extracted_metadata["generation_name"] == "responses_generation" + assert extracted_metadata["trace_id"] == "custom_trace_id" + + def test_responses_api_langfuse_specific_attributes(self): + """Test that ResponsesAPI metadata maps correctly to Langfuse OTEL attributes.""" + metadata = { + "generation_name": "responses_gen", + "generation_id": "resp_gen_123", + "trace_name": "responses_trace", + "trace_user_id": "resp_user_456", + "session_id": "resp_session_789", + "tags": ["responses", "api", "test"], + "trace_metadata": {"source": "responses_api", "version": "1.0"} + } + + kwargs = { + "call_type": "responses", + "litellm_params": {"metadata": metadata} + } + + mock_span = MagicMock() + + with patch('litellm.integrations.arize._utils.safe_set_attribute') as mock_safe_set_attribute: + LangfuseOtelLogger._set_langfuse_specific_attributes(mock_span, kwargs) + + # Verify specific attributes were set + from litellm.types.integrations.langfuse_otel import LangfuseSpanAttributes + + expected_calls = [ + (mock_span, LangfuseSpanAttributes.GENERATION_NAME.value, "responses_gen"), + (mock_span, LangfuseSpanAttributes.GENERATION_ID.value, "resp_gen_123"), + (mock_span, LangfuseSpanAttributes.TRACE_NAME.value, "responses_trace"), + (mock_span, LangfuseSpanAttributes.TRACE_USER_ID.value, "resp_user_456"), + (mock_span, LangfuseSpanAttributes.SESSION_ID.value, "resp_session_789"), + (mock_span, LangfuseSpanAttributes.TAGS.value, json.dumps(["responses", "api", "test"])), + (mock_span, LangfuseSpanAttributes.TRACE_METADATA.value, + json.dumps({"source": "responses_api", "version": "1.0"})) + ] + + for expected_call in expected_calls: + mock_safe_set_attribute.assert_any_call(*expected_call) + diff --git a/tests/test_litellm/litellm_core_utils/test_core_helpers.py b/tests/test_litellm/litellm_core_utils/test_core_helpers.py index 32f3ad3f55..89cc11c40d 100644 --- a/tests/test_litellm/litellm_core_utils/test_core_helpers.py +++ b/tests/test_litellm/litellm_core_utils/test_core_helpers.py @@ -9,7 +9,11 @@ sys.path.insert( 0, os.path.abspath("../../..") ) # Adds the parent directory to the system path -from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs, safe_divide +from litellm.litellm_core_utils.core_helpers import ( + get_litellm_metadata_from_kwargs, + safe_divide, + safe_deep_copy +) def test_get_litellm_metadata_from_kwargs(): @@ -127,3 +131,43 @@ def test_safe_divide_weight_scenario(): expected_zero = [0, 0, 0] assert normalized_zero_weights == expected_zero, f"Expected {expected_zero}, got {normalized_zero_weights}" + + +def test_safe_deep_copy_with_non_pickleables_and_span(): + """ + Verify safe_deep_copy: + - does not crash when non-pickleables are present, + - preserves structure/keys, + - deep-copies JSON-y payloads (e.g., messages), + - keeps non-pickleables by reference, + - redacts OTEL span in the copy and restores it in the original. + """ + import threading + rlock = threading.RLock() + data = { + "metadata": {"litellm_parent_otel_span": rlock, "x": 1}, + "messages": [{"role": "user", "content": "hi"}], + "optional_params": {"handle": rlock}, + "ok": True, + } + + copied = safe_deep_copy(data) + + # Structure preserved + assert set(copied.keys()) == set(data.keys()) + + # Messages are deep-copied (new object, same content) + assert copied["messages"] is not data["messages"] + assert copied["messages"][0] == data["messages"][0] + + # Non-pickleable subtree kept by reference (no crash) + assert copied["optional_params"] is data["optional_params"] + assert copied["optional_params"]["handle"] is rlock + + # OTEL span: redacted in the copy, restored in original + assert copied["metadata"]["litellm_parent_otel_span"] == "placeholder" + assert data["metadata"]["litellm_parent_otel_span"] is rlock + + # Other simple fields unchanged + assert copied["ok"] is True + assert copied["metadata"]["x"] == 1 diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index 48a22dcc8a..3ab3455d01 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -438,6 +438,97 @@ async def test_e2e_generate_cold_storage_object_key_successful(): assert isinstance(result, str) +@pytest.mark.asyncio +async def test_e2e_generate_cold_storage_object_key_with_custom_logger_s3_path(): + """ + Test that _generate_cold_storage_object_key uses s3_path from custom logger instance. + """ + from datetime import datetime, timezone + from unittest.mock import MagicMock, patch + + from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup + + # Create test data + start_time = datetime(2025, 1, 15, 10, 30, 45, 123456, timezone.utc) + response_id = "chatcmpl-test-12345" + + # Create mock custom logger with s3_path + mock_custom_logger = MagicMock() + mock_custom_logger.s3_path = "storage" + + with patch("litellm.configured_cold_storage_logger", "s3_v2"), \ + patch("litellm.logging_callback_manager.get_active_custom_logger_for_callback_name") as mock_get_logger, \ + patch("litellm.integrations.s3.get_s3_object_key") as mock_get_s3_key: + + # Setup mocks + mock_get_logger.return_value = mock_custom_logger + mock_get_s3_key.return_value = "storage/2025-01-15/time-10-30-45-123456_chatcmpl-test-12345.json" + + # Call the function + result = StandardLoggingPayloadSetup._generate_cold_storage_object_key( + start_time=start_time, + response_id=response_id + ) + + # Verify logger was queried correctly + mock_get_logger.assert_called_once_with("s3_v2") + + # Verify the S3 function was called with the custom logger's s3_path + mock_get_s3_key.assert_called_once_with( + s3_path="storage", # Should use custom logger's s3_path + team_alias_prefix="", + start_time=start_time, + s3_file_name="time-10-30-45-123456_chatcmpl-test-12345" + ) + + # Verify the result + assert result == "storage/2025-01-15/time-10-30-45-123456_chatcmpl-test-12345.json" + + +@pytest.mark.asyncio +async def test_e2e_generate_cold_storage_object_key_with_logger_no_s3_path(): + """ + Test that _generate_cold_storage_object_key falls back to empty s3_path when logger has no s3_path. + """ + from datetime import datetime, timezone + from unittest.mock import MagicMock, patch + + from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup + + # Create test data + start_time = datetime(2025, 1, 15, 10, 30, 45, 123456, timezone.utc) + response_id = "chatcmpl-test-12345" + + # Create mock custom logger without s3_path + mock_custom_logger = MagicMock() + mock_custom_logger.s3_path = None # or could be missing attribute + + with patch("litellm.configured_cold_storage_logger", "s3_v2"), \ + patch("litellm.logging_callback_manager.get_active_custom_logger_for_callback_name") as mock_get_logger, \ + patch("litellm.integrations.s3.get_s3_object_key") as mock_get_s3_key: + + # Setup mocks + mock_get_logger.return_value = mock_custom_logger + mock_get_s3_key.return_value = "2025-01-15/time-10-30-45-123456_chatcmpl-test-12345.json" + + # Call the function + result = StandardLoggingPayloadSetup._generate_cold_storage_object_key( + start_time=start_time, + response_id=response_id + ) + + # Verify the S3 function was called with empty s3_path (fallback) + mock_get_s3_key.assert_called_once_with( + s3_path="", # Should fall back to empty string + team_alias_prefix="", + start_time=start_time, + s3_file_name="time-10-30-45-123456_chatcmpl-test-12345" + ) + + # Verify the result + assert result == "2025-01-15/time-10-30-45-123456_chatcmpl-test-12345.json" + + @pytest.mark.asyncio async def test_e2e_generate_cold_storage_object_key_not_configured(): """ diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py index 2fc710664e..df003850f7 100644 --- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py @@ -1589,4 +1589,282 @@ async def test_no_cache_control_no_cache_point(): # Tool message should only have tool result, no cachePoint tool_content = result[2]["content"] assert len(tool_content) == 1 - assert "toolResult" in tool_content[0] \ No newline at end of file + assert "toolResult" in tool_content[0] + + +# ============================================================================ +# Guarded Text Feature Tests +# ============================================================================ + +def test_guarded_text_wraps_in_guardrail_converse_content(): + """Test that guarded_text content type gets wrapped in guardrailConverseContent blocks.""" + from litellm.litellm_core_utils.prompt_templates.factory import _bedrock_converse_messages_pt + + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Regular text content"}, + {"type": "guarded_text", "text": "This should be guarded"}, + {"type": "text", "text": "More regular text"} + ] + } + ] + + result = _bedrock_converse_messages_pt( + messages=messages, + model="us.amazon.nova-pro-v1:0", + llm_provider="bedrock_converse" + ) + + # Should have 1 message + assert len(result) == 1 + assert result[0]["role"] == "user" + + # Should have 3 content blocks + content = result[0]["content"] + assert len(content) == 3 + + # First and third should be regular text + assert "text" in content[0] + assert content[0]["text"] == "Regular text content" + assert "text" in content[2] + assert content[2]["text"] == "More regular text" + + # Second should be guardrailConverseContent + assert "guardrailConverseContent" in content[1] + assert content[1]["guardrailConverseContent"]["text"] == "This should be guarded" + + +def test_guarded_text_with_system_messages(): + """Test guarded_text with system messages using the full transformation.""" + config = AmazonConverseConfig() + + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": [ + {"type": "text", "text": "What is the main topic of this legal document?"}, + {"type": "guarded_text", "text": "This is a set of very long instructions that you will follow. Here is a legal document that you will use to answer the user's question."} + ] + } + ] + + optional_params = { + "guardrailConfig": { + "guardrailIdentifier": "gr-abc123", + "guardrailVersion": "DRAFT" + } + } + + result = config._transform_request( + model="us.amazon.nova-pro-v1:0", + messages=messages, + optional_params=optional_params, + litellm_params={}, + headers={} + ) + + # Should have system content blocks + assert "system" in result + assert len(result["system"]) == 1 + assert result["system"][0]["text"] == "You are a helpful assistant." + + # Should have 1 message (system messages are removed) + assert "messages" in result + assert len(result["messages"]) == 1 + + # User message should have both regular text and guarded text + user_message = result["messages"][0] + assert user_message["role"] == "user" + content = user_message["content"] + assert len(content) == 2 + + # First should be regular text + assert "text" in content[0] + assert content[0]["text"] == "What is the main topic of this legal document?" + + # Second should be guardrailConverseContent + assert "guardrailConverseContent" in content[1] + assert content[1]["guardrailConverseContent"]["text"] == "This is a set of very long instructions that you will follow. Here is a legal document that you will use to answer the user's question." + + +def test_guarded_text_with_mixed_content_types(): + """Test guarded_text with mixed content types including images.""" + from litellm.litellm_core_utils.prompt_templates.factory import _bedrock_converse_messages_pt + + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Look at this image"}, + {"type": "image_url", "image_url": {"url": "data:image/png;base64,test"}}, + {"type": "guarded_text", "text": "This sensitive content should be guarded"} + ] + } + ] + + result = _bedrock_converse_messages_pt( + messages=messages, + model="us.amazon.nova-pro-v1:0", + llm_provider="bedrock_converse" + ) + + # Should have 1 message + assert len(result) == 1 + assert result[0]["role"] == "user" + + # Should have 3 content blocks + content = result[0]["content"] + assert len(content) == 3 + + # First should be regular text + assert "text" in content[0] + assert content[0]["text"] == "Look at this image" + + # Second should be image + assert "image" in content[1] + + # Third should be guardrailConverseContent + assert "guardrailConverseContent" in content[2] + assert content[2]["guardrailConverseContent"]["text"] == "This sensitive content should be guarded" + + +@pytest.mark.asyncio +async def test_async_guarded_text(): + """Test async version of guarded_text processing.""" + from litellm.litellm_core_utils.prompt_templates.factory import BedrockConverseMessagesProcessor + + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Hello"}, + {"type": "guarded_text", "text": "This should be guarded"} + ] + } + ] + + result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async( + messages=messages, + model="us.amazon.nova-pro-v1:0", + llm_provider="bedrock_converse" + ) + + # Should have 1 message + assert len(result) == 1 + assert result[0]["role"] == "user" + + # Should have 2 content blocks + content = result[0]["content"] + assert len(content) == 2 + + # First should be regular text + assert "text" in content[0] + assert content[0]["text"] == "Hello" + + # Second should be guardrailConverseContent + assert "guardrailConverseContent" in content[1] + assert content[1]["guardrailConverseContent"]["text"] == "This should be guarded" + + +def test_guarded_text_with_tool_calls(): + """Test guarded_text with tool calls in the conversation.""" + from litellm.litellm_core_utils.prompt_templates.factory import _bedrock_converse_messages_pt + + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What's the weather?"}, + {"type": "guarded_text", "text": "Please be careful with sensitive information"} + ] + }, + { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "call_123", + "type": "function", + "function": {"name": "get_weather", "arguments": "{}"} + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_123", + "content": "It's sunny and 25°C" + } + ] + + result = _bedrock_converse_messages_pt( + messages=messages, + model="us.amazon.nova-pro-v1:0", + llm_provider="bedrock_converse" + ) + + # Should have 3 messages + assert len(result) == 3 + + # First message (user) should have both text and guarded_text + user_message = result[0] + assert user_message["role"] == "user" + content = user_message["content"] + assert len(content) == 2 + + # First should be regular text + assert "text" in content[0] + assert content[0]["text"] == "What's the weather?" + + # Second should be guardrailConverseContent + assert "guardrailConverseContent" in content[1] + assert content[1]["guardrailConverseContent"]["text"] == "Please be careful with sensitive information" + + # Other messages should not have guardrailConverseContent + for i in range(1, 3): + content = result[i]["content"] + for block in content: + assert "guardrailConverseContent" not in block + + +def test_guarded_text_guardrail_config_preserved(): + """Test that guardrailConfig is preserved when using guarded_text.""" + config = AmazonConverseConfig() + + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Hello"}, + {"type": "guarded_text", "text": "This should be guarded"} + ] + } + ] + + optional_params = { + "guardrailConfig": { + "guardrailIdentifier": "gr-abc123", + "guardrailVersion": "DRAFT" + } + } + + result = config._transform_request( + model="us.amazon.nova-pro-v1:0", + messages=messages, + optional_params=optional_params, + litellm_params={}, + headers={} + ) + + # GuardrailConfig should be present at top level + assert "guardrailConfig" in result + assert result["guardrailConfig"]["guardrailIdentifier"] == "gr-abc123" + + # GuardrailConfig should also be in inferenceConfig + assert "inferenceConfig" in result + assert "guardrailConfig" in result["inferenceConfig"] + assert result["inferenceConfig"]["guardrailConfig"]["guardrailIdentifier"] == "gr-abc123" + + diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py index c1cefa41ae..bec60434c2 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py @@ -704,3 +704,177 @@ class TestVertexBase: vertex_base.get_api_base(api_base=api_base, vertex_location=vertex_location) == expected ), f"Expected {expected} with api_base {api_base} and vertex_location {vertex_location}" + + @pytest.mark.parametrize( + "api_base, custom_llm_provider, gemini_api_key, endpoint, stream, auth_header, url, model, expected_auth_header, expected_url", + [ + # Test case 1: Gemini with custom API base + ( + "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta", + "gemini", + "test-api-key", + "generateContent", + False, + None, + "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent", + "gemini-2.5-flash-lite", + "test-api-key", + "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" + ), + # Test case 2: Gemini with custom API base and streaming + ( + "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta", + "gemini", + "test-api-key", + "generateContent", + True, + None, + "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent", + "gemini-2.5-flash-lite", + "test-api-key", + "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent?alt=sse" + ), + # Test case 3: Non-Gemini provider with custom API base + ( + "https://custom-vertex-api.com", + "vertex_ai", + None, + "generateContent", + False, + "Bearer token123", + "https://aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-pro:generateContent", + "gemini-pro", + "Bearer token123", + "https://custom-vertex-api.com:generateContent" + ), + # Test case 4: No API base provided (should return original values) + ( + None, + "gemini", + "test-api-key", + "generateContent", + False, + "Bearer token123", + "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent", + "gemini-2.5-flash-lite", + "Bearer token123", + "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" + ), + # Test case 5: Gemini without API key (should raise ValueError) + ( + "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta", + "gemini", + None, + "generateContent", + False, + None, + "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent", + "gemini-2.5-flash-lite", + None, # This should raise an exception + None + ), + ], + ) + def test_check_custom_proxy( + self, + api_base, + custom_llm_provider, + gemini_api_key, + endpoint, + stream, + auth_header, + url, + model, + expected_auth_header, + expected_url + ): + """Test the _check_custom_proxy method for handling custom API base URLs""" + vertex_base = VertexBase() + + if custom_llm_provider == "gemini" and api_base and gemini_api_key is None: + # Test case 5: Should raise ValueError for Gemini without API key + with pytest.raises(ValueError, match="Missing gemini_api_key"): + vertex_base._check_custom_proxy( + api_base=api_base, + custom_llm_provider=custom_llm_provider, + gemini_api_key=gemini_api_key, + endpoint=endpoint, + stream=stream, + auth_header=auth_header, + url=url, + model=model, + ) + else: + # Test cases 1-4: Should work correctly + result_auth_header, result_url = vertex_base._check_custom_proxy( + api_base=api_base, + custom_llm_provider=custom_llm_provider, + gemini_api_key=gemini_api_key, + endpoint=endpoint, + stream=stream, + auth_header=auth_header, + url=url, + model=model, + ) + + assert result_auth_header == expected_auth_header, f"Expected auth_header {expected_auth_header}, got {result_auth_header}" + assert result_url == expected_url, f"Expected URL {expected_url}, got {result_url}" + + def test_check_custom_proxy_gemini_url_construction(self): + """Test that Gemini URLs are constructed correctly with custom API base""" + vertex_base = VertexBase() + + # Test various Gemini models with custom API base + test_cases = [ + ("gemini-2.5-flash-lite", "generateContent", "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent"), + ("gemini-2.5-pro", "generateContent", "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent"), + ("gemini-1.5-flash", "streamGenerateContent", "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:streamGenerateContent"), + ] + + for model, endpoint, expected_url in test_cases: + _, result_url = vertex_base._check_custom_proxy( + api_base="https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta", + custom_llm_provider="gemini", + gemini_api_key="test-api-key", + endpoint=endpoint, + stream=False, + auth_header=None, + url=f"https://generativelanguage.googleapis.com/v1beta/models/{model}:{endpoint}", + model=model, + ) + + assert result_url == expected_url, f"Expected {expected_url}, got {result_url} for model {model}" + + def test_check_custom_proxy_streaming_parameter(self): + """Test that streaming parameter correctly adds ?alt=sse to URLs""" + vertex_base = VertexBase() + + # Test with streaming enabled + _, result_url_streaming = vertex_base._check_custom_proxy( + api_base="https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta", + custom_llm_provider="gemini", + gemini_api_key="test-api-key", + endpoint="generateContent", + stream=True, + auth_header=None, + url="https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent", + model="gemini-2.5-flash-lite", + ) + + expected_streaming_url = "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent?alt=sse" + assert result_url_streaming == expected_streaming_url, f"Expected {expected_streaming_url}, got {result_url_streaming}" + + # Test with streaming disabled + _, result_url_no_streaming = vertex_base._check_custom_proxy( + api_base="https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta", + custom_llm_provider="gemini", + gemini_api_key="test-api-key", + endpoint="generateContent", + stream=False, + auth_header=None, + url="https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent", + model="gemini-2.5-flash-lite", + ) + + expected_no_streaming_url = "https://proxy.zapier.com/generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" + assert result_url_no_streaming == expected_no_streaming_url, f"Expected {expected_no_streaming_url}, got {result_url_no_streaming}" diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_session_handler.py b/tests/test_litellm/responses/litellm_completion_transformation/test_session_handler.py index 2e1fe2241a..637f8d449a 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_session_handler.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_session_handler.py @@ -364,3 +364,53 @@ async def test_should_check_cold_storage_for_full_payload(): with patch.object(litellm, 'configured_cold_storage_logger', None): result5 = ResponsesSessionHandler._should_check_cold_storage_for_full_payload(proxy_request_with_truncated_pdf) assert result5 == False, "Should return False when cold storage is not configured, even with truncated content" + + +@pytest.mark.asyncio +async def test_get_chat_completion_message_history_empty_response_dict(): + """ + Test that empty response dict is handled correctly without processing. + This tests the fix for response validation to check for empty dict responses. + """ + from unittest.mock import AsyncMock, patch + + # Mock spend logs with empty response dict + mock_spend_logs = [ + { + "request_id": "chatcmpl-test-empty-response", + "call_type": "aresponses", + "api_key": "test_key", + "spend": 0.001, + "total_tokens": 0, + "prompt_tokens": 0, + "completion_tokens": 0, + "startTime": "2025-01-15T10:30:00.000+00:00", + "endTime": "2025-01-15T10:30:01.000+00:00", + "model": "gpt-4", + "session_id": "test-session", + "proxy_server_request": { + "input": "test input", + "model": "gpt-4" + }, + "response": {} # Empty dict - should not be processed + } + ] + + with patch.object(ResponsesSessionHandler, "get_all_spend_logs_for_previous_response_id") as mock_get_spend_logs: + mock_get_spend_logs.return_value = mock_spend_logs + + # Call the function + result = await ResponsesSessionHandler.get_chat_completion_message_history_for_previous_response_id( + "chatcmpl-test-empty-response" + ) + + # Verify that user message was added but no assistant response + # Since response is empty dict, no assistant response should be processed + # But user input from proxy_server_request should still be included + messages = result["messages"] + assert len(messages) == 1 # Only user message, no assistant response + assert messages[0]["role"] == "user" + assert messages[0]["content"] == "test input" + + # Verify the session was still created correctly + assert result["litellm_session_id"] == "test-session" diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 0c7aa52724..fc7088f4b6 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -508,6 +508,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "supports_computer_use": {"type": "boolean"}, "cache_creation_input_audio_token_cost": {"type": "number"}, "cache_creation_input_token_cost": {"type": "number"}, + "cache_creation_input_token_cost_above_1hr": {"type": "number"}, "cache_creation_input_token_cost_above_200k_tokens": {"type": "number"}, "cache_read_input_token_cost": {"type": "number"}, "cache_read_input_token_cost_above_200k_tokens": {"type": "number"}, @@ -661,16 +662,16 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "type": "array", "items": {"type": "number"}, "minItems": 2, - "maxItems": 2 + "maxItems": 2, }, "input_cost_per_token": {"type": "number"}, "output_cost_per_token": {"type": "number"}, "cache_read_input_token_cost": {"type": "number"}, - "output_cost_per_reasoning_token": {"type": "number"} + "output_cost_per_reasoning_token": {"type": "number"}, }, "required": ["range"], - "additionalProperties": False - } + "additionalProperties": False, + }, }, }, "additionalProperties": False, @@ -843,7 +844,6 @@ for commitment in BEDROCK_COMMITMENTS: print("block_list", block_list) - def test_supports_computer_use_utility(): """ Tests the litellm.utils.supports_computer_use utility function. @@ -925,8 +925,7 @@ def test_pre_process_non_default_params(model, custom_llm_provider): from litellm.utils import ProviderConfigManager, pre_process_non_default_params provider_config = ProviderConfigManager.get_provider_chat_config( - model=model, - provider=LlmProviders(custom_llm_provider) + model=model, provider=LlmProviders(custom_llm_provider) ) class ResponseFormat(BaseModel): diff --git a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx index 373fb18ade..3771ea5b1b 100644 --- a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx +++ b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx @@ -857,10 +857,6 @@ const CreateKey: React.FC = ({ className="mt-4" help={premiumUser ? "Select existing guardrails or enter new ones" : "Premium feature - Upgrade to set guardrails by key"} > - = ({ } options={promptsList.map(name => ({ value: name, label: name }))} /> - - -